Multimodality Professors and Supervisors | ApplySharif

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Lin WangView profile

Lecturer

Queen Mary University of LondonUnited Kingdom

Dr. Lin Wang is a Lecturer in Applied Data Science and Signal Processing at Queen Mary University of London (QMUL), affiliated with the School of Electronic Engineering and Computer Science. He leads the Machine Listening Lab and is a member of the Centre for Multimodal AI, Centre for Intelligent Sensing (CIS), and Institute of Coding (IoC). His research focuses on audio-visual signal processing, robotic perception, and machine learning, with applications in healthcare, drone-based sensing, and human activity recognition. Dr. Wang holds a PhD from Dalian University of Technology and has held postdoctoral positions at QMUL, the University of Sussex, and the Alexander von Humboldt Foundation in Germany. Education and Roles: PhD in Signal Processing, Dalian University of Technology (2010) Postdoc at Queen Mary University of London (2014–2017) Postdoc at University of Sussex (2017–2018) Alexander von Humboldt Fellow at University of Oldenburg (2011–2013) Fellow of the Higher Education Academy (UK) Research Interests: Audio-visual signal processing for drones and wearable devices Machine listening and robotic perception Machine learning for healthcare and environmental monitoring Human activity recognition using multimodal sensors Awards and Grants: Early Career Champion on AI&Data, UK Acoustics Network Outstanding Article Award, Frontiers in Computer Science (2022) EPSRC grant: Bioacoustic Monitoring Using Drones (£46,821, 2022–2023) Innovate UK grant: Music Source Separation (£48,144, 2024–2025) Teaching and Students: Dr. Wang teaches Applied Statistics , Website Design and Authoring , and Machine Learning for Visual Data Analysis . He supervises PhD students including Ashish Alex (speech separation), Michael Clayton (drone audition), and Dmitrii Mukhutdinov (audio-visual processing). Labs and Teams: He co-leads the Machine Listening Lab and is part of the Centre for Multimodal AI, focusing on interdisciplinary projects in robotics, acoustics, and AI.

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Alex WarstadtView profile

Assistant Professor

University of California, San DiegoUnited States

Alex Warstadt is an Assistant Professor at the University of California San Diego, holding appointments in the Department of Linguistics and the Halıcıoğlu Data Science Institute (HDSI). His research focuses on computational linguistics, applying advances in Large Language Models (LLMs) to understand human language acquisition, processing, and structure. Key contributions include developing the CoLA and BLiMP benchmarks for evaluating grammatical ability in LLMs, and the BabyLM Challenge to promote data-efficient language models. His work bridges theoretical linguistics, experimental methods, and computational modeling, particularly in pragmatics and discourse structure. Education: He earned B.A.s in Linguistics and Music Theory from Brown University and a Ph.D. in Linguistics from New York University (NYU), with a dissertation on 'Artificial Neural Networks as Models of Human Language Acquisition.' Postdoctoral work at ETH Zürich furthered his interdisciplinary research. He leads the LeM🍋N Lab at UC San Diego, which investigates language learning, meaning representation, and natural language processing through interdisciplinary collaboration. Research Interests: Warstadt’s research emphasizes leveraging machine learning to explore developmental linguistics, computational cognitive modeling, and pragmatic phenomena such as relevance and presupposition. His lab’s work aims to create models that align with human learning processes while advancing efficient NLP techniques. Recent projects include studying multimodal input effects and optimizing models for developmental plausibility. Labs/Teams: Director of the Learning, Meaning, and Natural Language (LeM🍋N) Lab, focusing on interdisciplinary research across linguistics, cognitive science, and data science.

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Wesley WillettView profile

Associate Professor

University of CalgaryCanada

Wesley Willett is an Associate Professor in the Department of Computer Science at the University of Calgary, holding the NSERC CRC II Chair in Visual Analytics. His primary research focuses on information visualization, human-computer interaction, and new media applications. He leads the Data Experience Lab and Interactions Lab, exploring innovative methods for data representation and interaction in augmented/virtual reality environments. Education includes a B.S. in Computer Science from the University of Colorado (2006) and a Ph.D. in Computer Science from UC Berkeley (2012). His work bridges technical innovation with user-centered design principles, emphasizing ethical considerations in data visualization and inclusive representation. Key research contributions include: spatial visualization techniques for large environments, gesture-based interfaces for AR/VR, and physical data representations through projects like Cetonia (swarm robotics visualization) and Data Embroidery. His work has been recognized with Best Paper awards at CHI 2015 and Pervasive 2010. Current research emphasizes immersive analytics, wearable visualization systems, and demographically diverse anthropographics. He collaborates with urban designers, neurologists, and environmental scientists to apply visualization in diverse domains like epilepsy surgery planning and air quality monitoring.

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Larry HeckView profile

Professor

Georgia Institute of TechnologyUnited States

Larry Heck is a Professor at the Georgia Institute of Technology with joint appointments in the School of Electrical and Computer Engineering and the School of Interactive Computing. He holds the Rhesa S. Farmer Advanced Computing Concepts Chair and is a Georgia Research Alliance Eminent Scholar. His research focuses on machine learning, deep learning, natural language processing, conversational systems, and speech/speaker recognition. He directs the AI Virtual Assistant (AVA) Lab, advancing next-generation AI assistants. Dr. Heck has held leadership roles in industry, including at Microsoft, Google, Samsung, and Viv Labs, and has over 50 U.S. patents. Education: BSEE from Texas Tech University (1986) MSEE and PhD in Electrical Engineering from Georgia Tech (1991) Research Interests: Dr. Heck’s work bridges machine learning and human-centric AI, with emphasis on conversational systems, multimodal interaction, and real-world applications. His AVA Lab develops AI assistants that integrate visual, auditory, and contextual cues for natural interaction. Recent projects include multimodal sensor integration, dialogue systems for caregiving networks, and embodied AI for avatar animation. Awards: IEEE Fellow (2020) Academy of Distinguished Engineering Alumni, Georgia Tech (2017) Distinguished Engineer Award, Texas Tech University (2017) Advising & Grants: While primarily focused on industry collaboration, Dr. Heck mentors students through Georgia Tech’s interdisciplinary programs. His research is funded by government agencies and corporate partnerships, including the NSA and DARPA. Labs & Teams: The AVA Lab collaborates with academia and industry to create AI systems that understand context, gestures, and environment. Current initiatives include multimodal dialogue datasets (e.g., OKCV, SensorQA) and reinforcement learning frameworks for real-time systems.

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Truong Q. NguyenView profile

Professor

University of California, San DiegoUnited States

Truong Q. Nguyen is a Professor in the Electrical and Computer Engineering (ECE) Department at the University of California San Diego (UCSD), affiliated with the Jacobs School of Engineering. He holds positions at the Center for Wireless Communications and the California Institute for Telecommunications and Information Technology. His research focuses on image/video processing, wavelets, 3D video technology, and applications in healthcare and robotics. He has authored influential textbooks like Wavelets & Filter Banks and pioneered low-power video processing algorithms for mobile devices. Nguyen earned his B.S., M.S., and Ph.D. in Electrical Engineering from the California Institute of Technology (1985–1989). He held roles at MIT Lincoln Laboratory and Boston University before joining UCSD in 1998. His honors include the IEEE Signal Processing Paper Award (1992), NSF Career Award (1995), IEEE Fellow (2005), and UCSD’s Distinguished Teaching Award (2019). His research interests span 3D video processing, machine learning for health monitoring, and biomedical imaging. Notable contributions include wavelet-based compression techniques and AI-driven medical image analysis. He leads the UCSD Video Processing Lab, exploring computer vision, robotics, and generative AI applications. Nguyen is committed to educational innovation, co-creating programs like the Hands-on Curriculum, Summer Research Internship Program (SRIP), and Project-in-a-Box (PIB) for K-12 students. Nguyen’s work bridges academia and industry, with patents in wavelet design and signal analysis. Recent projects include NSF-funded initiatives to develop inclusive engineering curricula and collaborate on graduate pathways programs through the Inclusive Engineering Consortium (IEC).

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Weihan LiView profile

Assistant Professor

RWTH Aachen UniversityGermany

Prof. Dr.-Ing. Weihan Li is a Junior Professor at RWTH Aachen University, specializing in Artificial Intelligence and Digitalization for Batteries. He is affiliated with the Institute for Power Electronics and Electrical Drives (ISEA) and the Center for Ageing, Reliability, and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL). His research bridges informatics, electrochemistry, and power electronics to advance battery technology through AI. B.Sc. in Automotive Engineering (Tongji University, 2014) M.Sc. in Automotive Engineering and Transport (RWTH Aachen, 2017) Ph.D. in Electrical Engineering and Information Technology (RWTH Aachen, 2021, summa cum laude) Prof. Li’s research focuses on AI-driven battery modeling, diagnostics, and optimization. Key areas include digital twin technology, electrochemical parameterization, and lifetime prediction using field data. He explores multi-scale kinetic processes, thermal management, and mechanical-electrochemical coupling effects in battery systems. The articles listed reflect his leadership in AI-powered battery analytics, spanning degradation prediction, fast charging, failure mode analysis, and grid-scale storage. His work emphasizes both theoretical innovation (e.g., diffusion models, physics-informed neural networks) and practical applications (e.g., second-life battery screening, automotive integration). Clarivate Highly Cited Researcher 2024 BMBF BattFutur Research Group (€2M+) German Thesis Award (Körber Foundation) Reichart Prize vgbe Innovation Prize Battery Young Research Award Umbrella Award RWTH Innovation Award Prof. Li leads an interdisciplinary research group with over €6 million in grants from BMBF, BMWK, BMDV, European Commission, and industry partners. His teams focus on battery informatics, AI-driven diagnostics, and digitalization of testing processes at CARL and ISEA.

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Lu ShijianView profile

Associate Professor

Nanyang Technological UniversitySingapore

Lu Shijian is an Associate Professor (tenured) at the School of Computer Science and Engineering , Nanyang Technological University (NTU) , Singapore. He holds a PhD in Electrical and Computer Engineering from the National University of Singapore and leads the Visual Intelligence Lab (VILab) , focusing on humanlike visual perception, understanding, and creation. University: Nanyang Technological University School: School of Computer Science and Engineering Academic Rank: Associate Professor (tenured) Email: Shijian.Lu@ntu.edu.sg Office: N4-02C-101, NTU, Singapore His research spans computer vision, deep learning, image and video analytics, visual intelligence, and machine learning , with key topics including scene text detection, unsupervised domain adaptation, image synthesis, satellite image analytics, and facial expression recognition. His work integrates supervised, semi-supervised, and self-supervised learning across 2D images, 3D point clouds, and multi-spectral data. The recent publications highlight a strong trend in domain adaptation, generative modeling, 3D vision, and multimodal AI . His lab produces high-impact work accepted at top venues like CVPR, ICCV, ECCV, NeurIPS, and TPAMI, with applications in autonomous systems, image editing, and robust AI. Top winner of ICFHR2014 Competition on Word Recognition from Historical Documents Top winner of ICDAR 2013 Robust Reading Competition (scene text segmentation) Top winner of ICDAR 2013 Document Image Binarization Contest (DIBCO 2013) Top winner of H-DIBCO 2010 – Handwritten Document Image Binarization Competition Top winner of ICDAR 2009 Document Image Binarization Contest (DIBCO 2009) Lu advises several PhD students and serves as an Associate Editor for Pattern Recognition and Neurocomputing . He has held leadership roles in top conferences as Senior Program Committee member (IJCAI, AAAI) and Area Chair (ICDAR, WACV). His lab, the Visual Intelligence Lab , is actively recruiting PhD students and conducting cutting-edge research in visual intelligence, with recent work on 3D Gaussian splatting, backdoor attacks, and vision-language models.

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Sanna JärveläView profile

Professor

University of OuluFinland

Sanna Järvelä is a Professor in the Faculty of Education and Psychology at the University of Oulu, where she leads the Learning and Educational Technology Research Lab (LET). She is a leading figure in learning sciences, with a focus on self-regulated learning, collaborative learning, and AI integration in educational contexts. Her research is supported by major international and national grants, including from the Jacobs Foundation. Research Interests: Self-regulated learning and socially shared regulation Computer-supported collaborative learning (CSCL) Artificial intelligence and adaptive technologies in education Development and application of multimodal research methods Her work bridges theoretical advancement with innovative methodological practices, contributing significantly to the understanding of learning in digital environments. While no specific publications are listed, her research output is extensive and highly cited, evidenced by her inclusion in Stanford University’s top 2% most-cited scientists list (2024). Scientific Awards and Honors: Member of the Finnish Academy of Science and Letters (2015) Francqui Chair, Ghent University (2015–2016) Learning Sciences Fellow (2022) Knight First Class of the Order of the White Rose of Finland (2022) Finnish Ostrobothnia Cultural Foundation Science Prize (2023) Stanford University’s Top 2% Scientist List (2024) Advising and Grants: As the head of the LET Lab and co-PI of the Center for Learning and Living with AI (CELLA), funded by the Jacobs Foundation, she mentors researchers and leads large-scale interdisciplinary projects. She also leads the University of Oulu’s Profi 7 Hybrid Intelligence research programme, shaping strategic research directions in AI and education. Labs and Teams: She founded and leads the Learning and Educational Technology Research Lab (LET), a vibrant research group focused on advancing knowledge about technology-enhanced learning. She is also a member of the OECD PISA 2025 ‘Learning in the Digital World’ expert team, influencing global educational policy and assessment frameworks.

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Sunny-Boy Ahmar MahboobView profile

Associate Professor

University of SydneyAustralia

Associate Professor Sunny-Boy Ahmar Mahboob is a distinguished linguist at the University of Sydney, where he serves as Degree Coordinator for the Master of Crosscultural and Applied Linguistics program within the Department of Linguistics, Faculty of Arts and Social Sciences. With extensive experience in applied linguistics and language education, Dr. Mahboob has established himself as a leading expert in World Englishes, TESOL, and language variation research, both nationally and internationally. Dr. Mahboob's educational background includes: BA from Karachi MA from Karachi MA from Indiana University MEd from Sydney PhD from Indiana University (2003) Dr. Mahboob's primary research focuses on language variation in global contexts, with particular attention to World Englishes, the NNEST (Non-Native English Speaking Teachers) movement, language policy, and decolonial approaches to linguistics. His work critically examines how language policies and practices affect educational outcomes and social justice, especially in postcolonial contexts like Pakistan and the Middle East. He pioneered 'appliable linguistics' - an approach that bridges theoretical linguistics with practical applications in education and policy, emphasizing the need for linguistic theories to address real-world problems. His recent publications reveal a strong trajectory toward decolonial linguistics, with increasing focus on subaltern perspectives, environmental connections to language diversity, and critical examinations of colonial legacies in language education. Dr. Mahboob's work consistently addresses power dynamics in language teaching and the need for more inclusive approaches that value linguistic diversity across educational contexts from primary schools to universities. Dr. Mahboob has received numerous prestigious awards: 2019: Australia's Research Field Leader in English Language and Literature (The Australian's Research magazine) 2012: Fellow of the National Talent Pool; President's Programme for Highly Qualified Overseas Pakistanis 2006: Faculty of Arts and Social Sciences Teaching Award, University of Sydney 2005: Teaching Initiative Award and Fellow of the Association of Pacific Rim Universities 2004: ECU Research and Creativity Award 2002: 'Distinguished President Award' from INTESOL, Indianapolis As an academic mentor, Dr. Mahboob supervises research students including Ruohong CAO (Developing Mandarin Learning Materials for Young Learners in Australia through Positive Discourse Analysis) and Tracy GAO (Evaluating Chinese Language Textbooks: A Sociosemiotic Perspective). His editorial work includes serving as Editor of TESOL Quarterly (2014-2017) and Associate Editor for multiple linguistics journals. Dr. Mahboob has secured significant research funding for projects including the SLATE (Scaffolding Literacy in Academic and Tertiary Environments) project and indigenous language funding initiatives in Australia. Dr. Mahboob founded and leads the International Free Linguistics Conference series, which has become an important platform for critical and alternative approaches to linguistics. His 'SLATE project' focuses on genre-based pedagogy for academic writing, and he actively collaborates with researchers across multiple continents through the World Englishes research network. His recent work increasingly explores the connections between language diversity and environmental sustainability, positioning linguistic ecology as a critical component of broader ecological concerns.

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Deepa KundurView profile

Professor

University of TorontoCanada

Deepa Kundur is the Professor & Chair of The Edward S. Rogers Sr. Department of Electrical & Computer Engineering at the University of Toronto. She earned her BASc, MASc, and PhD in Electrical and Computer Engineering from the same institution in 1993, 1995, and 1999, respectively. Current roles: IEEE Spectrum Advisory Board Conference leadership: General Chair of 2018 GlobalSIP Symposium, TPC Co-Chair for IEEE SmartGridComm 2018, among others Her research focuses on cybersecurity , signal processing , and complex dynamical networks , particularly in smart grid applications. She has authored over 200 publications and pioneered techniques for detecting false data injection attacks, enhancing grid resilience, and integrating machine learning into power systems. Her recent work spans quantum learning for grid security , LLM-based mental health prediction , and resilient control systems . She has received 14 best paper recognitions, including IEEE SmartGridComm (2015) and IEEE INFOCOM Workshop (2008). Fellowships: IEEE Fellow (2015), Canadian Academy of Engineering Fellow (2016), Massey College Senior Fellow (2019) Teaching awards: Tenneco Meritorious Teaching Award (2005), Gordon Slemon Teaching of Design Award (2002) Early career honors: NSERC Scholarships (PGS A/B), Canada Scholarship She leads the Kundur Research Group , developing models for cyber-physical systems in smart grids and autonomous vehicle networks. Her team explores reinforcement learning for grid defense , transmissibility-based fault detection , and privacy-preserving smart grid analytics .

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Vatsal SharanView profile

Assistant Professor

Brown UniversityUnited States

Vatsal Sharan is an Assistant Professor in the Thomas Lord Department of Computer Science at the University of Southern California's Viterbi School of Engineering. He maintains affiliations with the Theory Group, Machine Learning Center, and the Center for AI in Society at USC. Education: Ph.D. in Computer Science from Stanford University, advised by Greg Valiant Postdoctoral research at MIT, hosted by Ankur Moitra Vatsal Sharan's research centers on the theoretical foundations of machine learning, positioned at the intersection of machine learning, theoretical computer science, and statistics. His work investigates fundamental limits for solving learning and estimation tasks under computational and information-theoretic constraints, with the goal of developing practical algorithms that are efficient, fair, and robust. His research spans memory-efficient learning, algorithmic fairness, robustness in deep learning, and the theoretical underpinnings of transformers and large language models. A significant portion of his work explores how memory constraints affect learning algorithms and whether memory can serve as a distinguishing factor between 'efficient' and 'expensive' techniques in machine learning. His recent publications demonstrate a strong focus on multicalibration, transformer interpretability, and trustworthy AI systems. Scientific Awards: Amazon Research Award (2021 and 2023) SoCal NLP Symposium 2023 Best Paper Award COLT 2022 Best Paper Award Vatsal Sharan advises a diverse group of Ph.D. students including Siddartha Devic, Bhavya Vasudeva, Julian Asilis, Deqing Fu, Devansh Gupta, Spandan Senapati, and Tianyi Zhou. His research is supported by multiple prestigious grants from the NSF, Amazon Research, Google Research, and the Okawa Foundation. He is an active participant in the Learning Theory Alliance (LeT-All), a community-building and mentorship initiative for the learning theory community. His teaching portfolio includes advanced courses on machine learning theory and trustworthy machine learning at USC, where he shapes the next generation of researchers in theoretical aspects of artificial intelligence.

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Abhinav ShrivastavaView profile

Associate Professor

Brown UniversityUnited States

Abhinav Shrivastava is an Associate Professor in the Department of Computer Science at University of Maryland, College Park, with a joint appointment in the Institute of Advanced Computer Studies (UMIACS). Previously, he served as an Assistant Professor at the same institution from August 2018 to June 2024, and spent one year as a Visiting Research Scientist at Google Research from September 2017 to August 2018. His educational background includes: PhD in Robotics and Artificial Intelligence from Carnegie Mellon University (2017), advised by Abhinav Gupta, with thesis titled 'Discovering and Leveraging Visual Structure for Large-scale Recognition' MS in Artificial Intelligence from Carnegie Mellon University (2011), supervised by Alyosha Efros and Martial Hebert BTech in Computer Science and Engineering from Jaypee Institute of Information Technology (2010) Professor Shrivastava's research focuses on computer vision and machine learning, with particular expertise in object detection, image recognition, and neural representations. His work bridges theoretical advances with practical applications, exploring how visual systems can discover and leverage structure in large-scale recognition problems. He has made significant contributions to understanding the role of supervision in vision transformers, developing novel approaches for object-state composition recognition, and creating efficient neural representations for videos and 3D scenes. His research often addresses fundamental challenges in visual recognition, including handling novelty in open-world environments and improving the efficiency of visual systems. An analysis of his recent publications reveals a strong emphasis on neural representations, particularly for dynamic content like videos and 3D scenes. His work demonstrates increasing sophistication in handling open-world vision problems, with research spanning object discovery, localization, and representation learning. The publications show a clear progression toward more efficient and scalable models, with recent work focusing on model compression, sparse representations, and addressing the challenges of working with limited annotations. His scientific contributions have been recognized with several prestigious awards: Best Paper Award (Applications) at IEEE Winter Conference on Applications of Computer Vision (2020) Microsoft Research PhD Fellowship (2014-2016) Best Student Paper Award at IEEE Winter Conference on Applications of Computer Vision (2014) Outstanding Reviewer Award at IEEE CVPR (2015) Professor Shrivastava has successfully mentored numerous graduate students, many of whom have become prominent researchers in computer vision. His Amazon Research Awards (2020 and 2023) have supported innovative projects including 'The pursuit of knowledge: discovering and localizing new concepts using dual memory' and 'Audio-conditioned Diffusion Models for Generating Lip-synchronized Videos.' He has served as Area Chair for major conferences including ICCV, CVPR, and AAAI, demonstrating his leadership in the computer vision community. His research has attracted significant funding from both academic and industry sources, supporting his exploration of fundamental questions in visual recognition and representation learning.

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Chao LiuView profile

Researcher

University of StrasbourgFrance

Chao Liu is a Research Scientist at CNRS (French National Center for Scientific Research) since 2008, affiliated with the DEXTER team and the Department of Robotics, LIRMM at University of Montpellier, France. He earned his Ph.D. in Electrical & Electronic Engineering from Nanyang Technological University, Singapore (2006). Current research focuses on surgical robotics , haptics , teleoperation , and nonlinear control theory with applications in computer vision. His work addresses challenges in robotic-assisted telesurgery, including: Stable and transparent human-robot interaction through wave variable compensators and passivity filters Physiological motion compensation using spatio-temporal LSTM and dual Kalman filters EMG-based motion recognition for surgical skill assessment 3D soft-tissue reconstruction with stereo-endoscopes and deep learning Dr. Liu leads European and French projects like: TS2RT (CNRS-funded): Safer teleoperation with motion compensation ROBACUS (ANR-funded): Needle positioning with MPC control HaTUMoCo (CNRS-funded): Haptic teleoperation with uncertainty handling ARAKNES (EU-funded): Microrobotic systems for endoluminal surgery Scientific honors include Senior Member of IEEE and Member of Sigma Xi . He supervises Ph.D. and Master's students working on topics such as concentric tube robot optimization, haptic teleoperation, and EMG-based force estimation. Dr. Liu serves on IEEE Technical Committees for Telerobotics and Haptics , and as Technical Editor of IEEE/ASME Transactions on Mechatronics.

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Erick GuerraView profile

Professor

University of PennsylvaniaUnited States

Erick Guerra is a Professor and Associate Dean for Research at the Stuart Weitzman School of Design , University of Pennsylvania, specializing in transportation planning and urban development. He teaches courses on transportation planning, multimodal systems, and studio projects focused on transit-housing integration in Latin America. Education: Ph.D. in City and Regional Planning, University of California Berkeley Master of Urban Planning, Harvard University B.A. from University of Pennsylvania Guerra’s research examines the interconnections between transportation systems, land use patterns, and travel behavior, particularly in developing cities. His work addresses transportation equity, technology adoption, and public health impacts in urban environments. Recent publications analyze highway expansion economics ( Overbuilt: The High Costs and Low Rewards of US Highway Building , 2025), automated vehicle impacts on pedestrian behavior, speed camera effectiveness, and comparative studies of low-income commuting patterns in the U.S. and Mexico. Research trends highlight his focus on sustainable mobility, policy evaluation, and cross-cultural urban dynamics.

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Raymond J. MooneyView profile

Professor

University of Texas at AustinUnited States

Raymond J. Mooney is a Professor in the Department of Computer Science at the University of Texas at Austin, where he has been a faculty member since 1987. He is the Director of the UT Artificial Intelligence Laboratory and affiliated with multiple research groups including the Machine Learning Research Group, UT Computational Linguistics Lab, and the UT Center for Computational Biology and Bioinformatics. He holds a B.S., M.S., and Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign, where his thesis was supervised by Gerald DeJong. His research spans diverse areas in artificial intelligence, machine learning, and natural language processing: Natural Language Learning Connecting Language and Perception Statistical Relational Learning Information Extraction Transfer and Active Learning Abductive Reasoning Text Mining and Clustering Recommender Systems Knowledge-Base Refinement Recent publications highlight trends in grounded language processing, human-robot interaction, and multimodal reasoning. He has been recognized with prestigious fellowships including ACL (2014), ACM (2010), and AAAI (2005). Scientific awards: Fellow of the Association for Computational Linguistics (2014) Fellow of the Association for Computing Machinery (2010) Fellow of the American Association for Artificial Intelligence (2005) Classic Paper Award (2019) Best Paper Awards (2007, 2004, 1996) He teaches graduate courses like CS 371R: Information Retrieval and Web Search (Fall 2025) and CS 395T: Grounded Natural Language Processing (Spring 2025). His research labs include: UT Artificial Intelligence Laboratory Machine Learning Research Group UT Computational Linguistics Lab UT Center for Computational Biology and Bioinformatics

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