جستجوی استاد راهنمای دانشگاهی

Aniket Kitturمشاهده پروفایل

استاد

• AI-Augmented Cognition
• Collective Intelligence
• Social Computing
• +۷ مورد دیگر

Aniket (Niki) Kittur is a Professor in the Human-Computer Interaction Institute (HCII) at Carnegie Mellon University's School of Computer Science. His research focuses on AI-augmented cognition, exploring how humans and computational systems can partner to accelerate knowledge acquisition and innovation. With over 100 peer-reviewed publications and 17 best paper awards or honorable mentions, Dr. Kittur has established himself as a leader in human-computer interaction and social computing. Dr. Kittur's research spans multiple interconnected areas focused on enhancing human cognition through technology. His work on crowd-augmented cognition examines how crowds and computation can collectively enhance human intellect. In social computing, he investigates how sociotechnical architectures can motivate and coordinate large groups for complex tasks. His research in data visualization and sensemaking explores how people collect, organize, and make decisions with online information. Most recently, his work has shifted toward LLM-augmented cognition, investigating how humans and large language models can work together in partnership to achieve better decisions and greater creativity than either could alone. Dr. Kittur's publications reveal a consistent trajectory toward increasingly sophisticated human-AI collaboration systems. Early work focused on crowdsourcing complex tasks (CrowdForge, CrowdSynthesis), then evolved to knowledge acceleration systems (Knowledge Accelerator, Fuse), and has recently centered on LLM-integrated cognition (Selenite, InkSpire, BioSpark). His research consistently demonstrates practical impact, with systems influencing products used by millions and receiving coverage in major media outlets including Nature News, The Economist, and The Wall Street Journal. Kavli fellow NSF CAREER award recipient Allen Newell Award for Research Excellence Inductee into the CHI Academy 17 best paper awards or honorable mentions across his publications Dr. Kittur has advised numerous students, including Andrew Kuznetsov, and has secured major research funding from NSF, NIH, Google, Microsoft, Bosch, Toyota, and other organizations. His Skeema browser extension project, which addresses tab overload through innovative project-based organization, has shown remarkable user retention and represents a practical application of his research on knowledge acceleration. His work with industry partners has influenced products used by millions, including Semantic Reader, Google Shopping, and Wikipedia features.

Christos Faloutsosمشاهده پروفایل

استاد

• Data Mining
• Graph Mining
• Fractals and Power Laws
• +۳ مورد دیگر

Christos Faloutsos is the Fredkin Professor of Computer Science at Carnegie Mellon University, with a courtesy appointment in Electrical and Computer Engineering. He holds a B.Sc. from the National Technical University of Athens and M.Sc./Ph.D. from the University of Toronto. His research focuses on data mining, graph analysis, fractals, and database systems. Notable contributions include foundational work on R-trees, graph mining laws (e.g., Kronecker graphs), and applications in medical imaging, network security, and fraud detection. Key projects include PEGASUS (petascale graph mining), fraud detection in online auctions (NetProbe), and tools for human trafficking analysis (TrafficVis). He has led NSF-funded projects on tensor mining, network anomaly detection, and bioinformatics. Over 300 refereed publications highlight his contributions across databases, data mining, and networks. Awards include the KDD Best Paper (2005, 2016), SIGMOD Test-of-Time Award, and recognition as a top nurturer in IT. His lab collaborations span the Parallel Data Lab (PDL), Machine Learning Department, and Computational Biology.

• Embodied Artificial Intelligence
• Natural Language Processing
• Robotics
• +۳ مورد دیگر

Yonatan Bisk is an Assistant Professor at Carnegie Mellon University within the Language Technologies Institute (with courtesy appointment in Robotics Institute). His research bridges Natural Language Processing , Robotics , and Embodied AI , focusing on language grounding, theory of mind, and multimodal interaction. Education : Ph.D. in Computer Science from University of Illinois at Urbana-Champaign Postdoctoral Experience : USC ISI, University of Washington, Allen Institute for AI Industry Appointments : Microsoft Research, Meta AI His research emphasizes embodied language systems and social intelligence in AI . Recent projects include WebArena for autonomous agents, SOTOPIA for social reasoning, and HomeRobot for open-vocabulary manipulation. He leads the REAL Center (Robotics, Embodied AI, and Learning) to foster interdisciplinary collaboration. Key scientific awards include selection for the DARPA ISAT Study Group (2024). He teaches courses like "Talking to Robots" and "Multimodal Machine Learning" while serving as area chair/editor across NLP, Robotics, and ML communities.

Brendan T. O'Connorمشاهده پروفایل

دانشیار

• Natural Language Processing
• Computational Social Science
• Statistical Text Analysis
• +۲ مورد دیگر

Brendan T. O'Connor is an Associate Professor at the College of Information and Computer Sciences, University of Massachusetts Amherst, where he directs the SLANG Lab and serves as Associate Director of the Computational Social Science Institute. His research bridges statistical machine learning and natural language processing with social science applications, particularly using text data from news and social media to understand societal patterns. His work focuses on developing text analysis methods to answer social science questions in domains like political science and sociolinguistics. Current collaborative projects include combating misinformation, analyzing bias in news coverage, and developing tools for clinical discourse assessment using large language models. O'Connor's publications demonstrate a consistent focus on computational social science, with recent work exploring multilingual analysis, legal discourse patterns, and sociolinguistic variation. His methodological contributions span coreference resolution, event extraction, and argument mining. At UMass, he contributes to multiple research centers including the Computational Social Science Institute, UMass NLP group, and Centers for Data Science and Intelligent Information Retrieval. He teaches graduate seminars in natural language processing and maintains active collaborations across disciplines.

• Machine Learning
• Artificial Intelligence
• Combinatorial Optimization
• +۱۳ مورد دیگر

Yiming Yang is a Professor at the Language Technologies Institute and Machine Learning Department within the School of Computer Science at Carnegie Mellon University , where he has held faculty positions since 2003. His research spans foundational and applied aspects of machine learning , artificial intelligence , and scientific computing . Professor, Carnegie Mellon University (2003–Present) Associate Professor, Carnegie Mellon University (1996–2003) Yang's research focuses on LLM-based problem-solving agents , combinatorial optimization , and scalable oversight frameworks . His work explores diffusion models, Langevin dynamics, and Fourier neural operators for NP-hard problems, while advancing reinforcement learning techniques for self-play supervision and principle-driven fine-tuning of large language models. Recent publications highlight his contributions to code synthesis , PDE solving , and multi-agent reinforcement learning . Key methodologies include demonstration-guided control, retrieval-augmented reasoning, and test-time scaling laws. His team has developed frameworks like FEEDER for efficient in-context learning and μTransfer-FNO for zero-shot hyperparameter transfer in PDE solvers. Notable scientific achievements include: Best Student Paper Runner Up (2013) Best Theoretical Paper Award (1994) Best Theoretical Paper Award (1993) Yang has mentored over 20 PhD students and postdocs, including Shengyu Feng , Zhiqing Sun , and Aman Madaan , across domains like graph learning , extreme multi-label classification , and language model alignment .

Kathleen M. Carleyمشاهده پروفایل

استاد

• Network Science
• Social Cybersecurity
• Computational Social Science
• +۴ مورد دیگر

Kathleen M. Carley is a full professor at Carnegie Mellon University's School of Computer Science with courtesy appointments in Engineering and Public Policy, Heinz School, and Electrical and Computer Engineering. As director of the Center for Computational Analysis of Social and Organizational Systems (CASOS) and the Center for Informed Democracy and Social-Cybersecurity (IDeaS) , she leads interdisciplinary research at the intersection of network science, cognitive modeling, and cybersecurity. Ph.D. in Sociology from Harvard University SB degrees in Economics and Political Science from MIT Her research focuses on Dynamic Network Analysis (DNA) and Social-Cybersecurity (SC) , developing tools like ORA (network analysis), AutoMap (semantic mining), Construct (influence simulation), and BotHunter (bot detection). She has over 400 publications and 15+ active research projects addressing disinformation, cognitive security, and organizational resilience. Recent work examines LLM-powered bots , multi-platform misinformation dynamics , and public health analytics . As an IEEE Fellow, she contributes to standards in computational social science while teaching courses on network analysis and complex socio-technical systems.

Niki Kitturمشاهده پروفایل

استاد

• AI-Augmented Cognition
• Human-Machine Collaboration
• Sensemaking Systems
• +۴ مورد دیگر

Aniket 'Niki' Kittur is a Professor in the Human-Computer Interaction Institute at Carnegie Mellon University's School of Computer Science. His research focuses on AI-augmented cognition, exploring how human and machine intelligence can collaborate to enhance creativity, decision-making, and innovation. He leads projects like the Semantic Reader and Skeema browser extension, aiming to reduce cognitive overload through intelligent systems. Education: BA in Psychology & Computer Science from Princeton University; PhD in Cognitive Psychology from UCLA. His work bridges HCI, crowdsourcing, and cognitive science, with 100+ publications and 17 best paper awards. He advises industry partners including Google, Microsoft, and Toyota while maintaining a lab focused on real-world impact. Research interests center on accelerating knowledge acquisition via systems that scaffold sensemaking (e.g., Selenite for web exploration) and fostering analogical innovation through crowdsourced/AI hybrid approaches. Notable contributions include CrowdForge (human-machine workflows) and Kinetica (touch-based data visualization). Awards include NSF CAREER Award, Allen Newell Award, and CHI Academy membership. His lab's Skeema tool has achieved 79% 30-day retention in beta, reflecting impactful user-centered design principles. Current projects emphasize LLM integration for composite cognition, aiming to create systems where 'LLMs + Humans > Either Alone.' Funding来自NSF, NIH, ONR, and industry partners like Bosch and Wikimedia. Teaching includes PhD bootcamps and user-centered research courses. Over 100 students have contributed to his projects, many advancing to tech leadership roles.

Wenhao Dingمشاهده پروفایل

پژوهشگر

• Autonomous Systems
• Reinforcement Learning
• Causal Discovery
• +۳ مورد دیگر

Wenhao Ding is a Research Scientist at NVIDIA's Autonomous Vehicle Group, focusing on enhancing the safety and robustness of physical autonomous systems, particularly autonomous vehicles. His research integrates multi-modal large language models, reinforcement learning, and causal discovery to improve model reasoning capabilities. He holds a Ph.D. from Tsinghua University's Department of Electronic Engineering, with a thesis on 'Generative AI for Critical Digital Twins.' Key research interests include safety-critical scenario generation, causal representation learning, and offline reinforcement learning. His work emphasizes closed-loop simulation for autonomous systems and has led to contributions like the SafeBench benchmarking platform and the RealGen scenario generation framework. He has received the 2022 Qualcomm Innovation Fellowship. Notable collaborations include projects with Prof. Marco Pavone at Stanford and internships at Amazon Lab126 (Astro team) and Bosch Center for AI. He actively reviews for top conferences (ICML, NeurIPS, CVPR) and journals (IEEE T-ITS, RA-L). His recent focus on privacy risks in robotics and causal-aware driving models underscores his commitment to trustworthy AI systems. He organizes conferences like the 2024 IEEE International Automated Vehicle Validation Conference and co-hosted the Secure and Safe Autonomous Driving (SSAD) Workshop at CVPR 2023. His interdisciplinary work bridges theory and practice, addressing critical challenges in autonomous systems' safety and generalization.

Andrew O. Arnoldمشاهده پروفایل

استاد مدعو

• Machine Learning
• Natural Language Processing
• Quantitative Trading
• +۵ مورد دیگر

Andrew O. Arnold is a Principal Applied Machine Learning Engineer at Shopify and an Adjunct Professor at New York University's Tandon School of Engineering, Department of Finance and Risk Engineering. He earned his Ph.D. in Machine Learning from Carnegie Mellon University and a BA in Computer Science and Artificial Intelligence from Columbia University. Education Ph.D., Machine Learning, Carnegie Mellon University BA, Computer Science and Artificial Intelligence, Columbia University His research focuses on robust machine learning , developing models that perform well in low signal-to-noise regimes, handle distributional shifts (transfer learning), and extract features from unstructured data. Key applications include time series analysis and natural language processing in financial and other domains. Recent publications highlight work on large language models (LLMs) for code generation, including multitask pretraining, contrastive learning, and quantization techniques for efficiency. He has contributed to understanding model robustness and adapting NLP methods to dynamic market conditions. Arnold teaches NYU FRE GY 7871: News Analytics and Machine Learning , covering NLP and ML techniques for quantitative trading strategies. The course emphasizes practical applications of sentiment analysis, text relevance, and novelty detection in financial contexts. He has led teams at Amazon Web Services (AI Labs), served as Chief Scientist at Oracle Alpha, and worked at Microsoft Research, IBM Research, and other institutions. His technical expertise spans code generation , anomaly detection , and NLP for commerce , with patents in these areas.

Jamie Callanمشاهده پروفایل

استاد

• Information Retrieval
• Neural Search Engine Architectures
• Knowledge Graphs
• +۵ مورد دیگر

Jamie Callan is a Professor at Carnegie Mellon University's Language Technologies Institute (School of Computer Science), where he leads research in Information Retrieval and Neural Search Architectures . He teaches advanced courses on search engine design and mentors students in multiple programs. Research Focus: Federated retrieval, knowledge graph integration in search, ClueWeb dataset development, and neural approaches to document ranking Leadership: Past SIGIR Treasurer/Chair, Co-founding Editor of Foundations and Trends in IR, former TOIS Editor-in-Chief His recent work explores: Neural Retrieval: Latent vocabulary for sparse systems, hypothetical documents for dense vector retrieval Dataset Innovation: Maintenance and distribution of ClueWeb09, ClueWeb12, and ClueWeb22 datasets Search Efficiency: Selective search architectures with 90% reduced computational costs Scientific Recognition: International ACM SIGIR Conference Leadership Co-founding Editor-in-Chief, Foundations and Trends in IR Former Editor-in-Chief of ACM TOIS Dr. Callan's Lemur Project has produced Indri/Galago search engines and supported TREC evaluations through dataset contributions.

Claire Le Gouesمشاهده پروفایل

دانشیار

• Software Engineering
• Programming Languages
• Program Analysis
• +۹ مورد دیگر

Claire Le Goues is an Associate Professor in the School of Computer Science at Carnegie Mellon University , affiliated with the Software and Societal Systems Department (formerly Institute for Software Research). She holds a Ph.D. and M.S. in Computer Science from the University of Virginia and a B.A. in Computer Science from Harvard College. Her research focuses on software engineering with emphasis on program analysis , transformation , and search-based repair . She leads the squaresLab group and co-directs the REUSE@CMU summer program. Her work spans automated program improvement (stochastic/formal approaches), software assurance, quality metrics, and systems from open source to robotics. Recent scientific awards include the ACM FSE 2025 Test of Time Award Honorable Mention and the Presidential Early Career Award for Scientists and Engineers (PECASE) . She mentors students in software engineering and actively collaborates on projects like SearchRepair and GenProg , supporting empirical benchmarks such as ManyBugs and IntroClass . She teaches software engineering and program analysis at undergraduate, master’s, and doctoral levels, addressing challenges in scaling modern systems. Her lab focuses on software repair , code transformation , and AI-driven testing .

Malihe Alikhaniمشاهده پروفایل

استادیار

• AI Ethics
• Computational Linguistics
• Natural Language Processing
• +۳ مورد دیگر

Malihe Alikhani is an Assistant Professor at Khoury College of Computer Sciences , Northeastern University , a Visiting Fellow at the Brookings Institution specializing in AI policy, and serves as the Ethics Chair of the Association for Computational Linguistics . She is also a member of the Northeastern Ethics Institute . Her research focuses on developing AI systems that enhance communication, decision-making, and knowledge-sharing through rigorous integration of cognitive and social sciences with machine learning. Her work spans academic, policy, and applied research domains, emphasizing contextual AI systems. She leads the Contextual AI Lab , which develops models capturing human interpretation to support collaborative meaning construction between humans and machines. Her recent publications address uncertainty modeling in dialogue systems, bias mitigation in sign language processing, and ethical AI frameworks. Key contributions include: Advancing sign language understanding in NLP Developing uncertainty-aware dialogue systems Creating discourse-coherent task-oriented dialogue frameworks Formalizing equity in text generation Scientific Awards: Best Theme Paper Award (ACL 2021) Best Paper Award (UAI 2022) She mentors students and researchers in areas spanning sign language processing, dialogue systems, and ethical AI. Her lab collaborates with Deaf and Hard-of-Hearing communities and cognitive science experts to ensure inclusive AI development.

Pierre Jinghong Liangمشاهده پروفایل

استاد

• Artificial Intelligence in Accounting
• Graph Theory in Bookkeeping
• Economic Theory of Disclosure
• +۵ مورد دیگر

Professor Pierre Jinghong Liang is a faculty member at the Tepper School of Business, Carnegie Mellon University, holding the rank of Professor of Accounting since 2016. He earned his PhD in Business Administration (Accounting) from the University of Florida (1998), an MA in Economics from the University of New Hampshire (1991), and a BA in International Finance from Xiamen University (1990). His research spans accounting theory, machine learning applications in financial systems, economic disclosure frameworks, and graph-theoretic modeling of double-entry bookkeeping. Education: PhD (University of Florida), MA (University of New Hampshire), BA (Xiamen University) Professor Liang’s work bridges accounting, economics, and computational sciences, with publications in The Accounting Review , Journal of Accounting Research , American Economic Review , and machine learning venues like ACM Transactions on Knowledge Discovery from Data. His recent projects focus on AI-driven auditing, carbon disclosure regulations, and graph mining in accounting systems. He also teaches courses on text and graph analytics in business contexts. Scientific Awards: Excellence in the Classroom Award (Tepper School of Business) He has served as Visiting Professor at the HKU Business School (2022–2023) and collaborates with institutions like the PwC Center at CMU. His professional roles include founding the Accounting and Economics Society and holding officer positions in the Chinese Accounting Professors Association of North America (CAPANA).

• Machine Learning
• Computer Vision
• Natural Language Processing
• +۲ مورد دیگر

Chun-Liang Li is a Research Scientist at Apple MLR and an Affiliate Assistant Professor at the Allen School of Computer Science, University of Washington. He holds a Ph.D. in Machine Learning from Carnegie Mellon University (2014-2019) and a B.S./M.S. in Computer Science and Information Engineering from National Taiwan University (2008-2013). His research spans machine learning, computer vision, and natural language processing, with emphasis on representation learning, efficient model training, and multimodal understanding. Key focus areas include large language model optimization, contrastive learning techniques, document understanding systems, and domain adaptation methods. Recent publications demonstrate strong focus on LLM efficiency (dataset decomposition, distillation techniques), document AI (table reasoning, form extraction), and multimodal alignment (image-text retrieval, prefix conditioning). His work consistently appears in premier venues including NeurIPS, CVPR, ACL, and ICLR. Awards and Honors: IBM Ph.D. Fellowship (2018) Best student paper runner-up, IJCAI (2017) First Place in both tracks of KDD Cup (2011, 2013) He actively mentors students and collaborators, inviting contact for internship opportunities. Current industry-academic position bridges cutting-edge research with practical applications in machine learning systems.

Marios Savvidesمشاهده پروفایل

استاد

• Biometric Identification
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Marios Savvides is the Bossa Nova Robotics Professor of Artificial Intelligence and a Full Tenured Professor in the Electrical and Computer Engineering Department at Carnegie Mellon University (CMU). He is also the Founder and Director of the CyLab Biometrics Center. His research focuses on AI algorithms for biometrics, facial recognition, iris scanning, and object detection under challenging conditions. He holds a BEng from the University of Manchester, an MS in Robotics from CMU, and a PhD in Electrical and Computer Engineering from CMU. Education: BEng, Microelectronics Systems Engineering, University of Manchester Institute of Science and Technology (1997) MS, Robotics, Carnegie Mellon University (2000) PhD, Electrical and Computer Engineering, Carnegie Mellon University (2004) Research Interests: Core AI and machine learning for robust biometric systems Long-range iris capture and matching Low-shot object detection and scene understanding Applications in retail automation, airport security, and medical imaging Key Achievements: Recipient of seven Best Paper Awards and the 2022 PIPLA Inventor of the Year Recipient of the 2015 Edison Gold Award, 2018 Immigrant Entrepreneur Award, and 2020 AI Excellence Award Developed AI algorithms deployed in over 3 million ADT security cameras and Bossa Nova robots Spun off startups including HawXeye and Oosto, serving as CTO/Chief AI Scientist Grants and Partnerships: Collaborations with Bossa Nova Robotics, Egen, UltronAI, and CMKL University Research on facial recognition for stock market prediction and medical imaging privacy risks Labs and Teams: Director of the CyLab Biometrics Center Member of IEEE Biometric Council and contributor to the IEEE Certified Biometrics Professional program