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Alexander SchwingView profile

Associate Professor

• Machine Learning
• Computer Vision
• Structured Prediction
• +7 more

Alexander Schwing is an Associate Professor in the Department of Electrical and Computer Engineering and Computer Science at the University of Illinois at Urbana-Champaign, affiliated with the Coordinated Science Laboratory. His research focuses on machine learning and computer vision with applications in 3D scene understanding, generative modeling, and multi-agent systems. Education: Diploma in Electrical Engineering and Information Technology, Technical University of Munich (TUM) PhD in Computer Science, ETH Zurich Postdoctoral Fellow, University of Toronto Research Interests: Structured prediction in deep learning Generative adversarial networks and stability Multi-modal vision-language models 3D scene reconstruction from single images Embodied agent collaboration Semantic segmentation with temporal coherence Recent Publications: Highlight trends in neural rendering, video object segmentation, and reinforcement learning with applications to 3D modeling and multi-agent systems. Notable innovations include SAIL-VOS dataset for amodal segmentation and NeRFDeformer for single-view scene transformation. Scientific Awards: NSF CAREER Award, 3M and Amazon research awards, multiple student recognition awards, ETH Zurich PhD medal, and best paper at Intelligent Tutoring Systems 2014. Teaching: Offers graduate courses in Pattern Recognition (ECE 544) and Machine Learning (CS 446/ECE 449). Previously taught at University of Toronto and ETH Zurich. Labs & Collaborations: Leads research at Coordinated Science Laboratory (UIUC) with collaborations across University of Toronto, ETH Zurich, and industry partners like Samsung SAIT and Amazon.

Matthias NießnerView profile

Professor

• 3D Digitization
• Computer Graphics
• Computer Vision
• +7 more

Prof. Matthias Nießner is a Professor at the Technical University of Munich, leading the Visual Computing Lab. His research intersects computer graphics, vision, and AI, focusing on 3D reconstruction, semantic understanding, and AI-driven video synthesis. He holds a PhD from the University of Erlangen-Nuremberg (2013) and was a Visiting Assistant Professor at Stanford University (2013–2017). Notable awards include the ERC Starting Grant (2018), Nvidia Professorship Award, and Eurographics Young Researcher Award (2019). His work has been featured in mainstream media and led to startups like Synthesia Inc. Research spans Gaussian splatting, neural radiance fields, and generative AI for 3D avatars. Over 150 publications include SIGGRAPH, CVPR, and ECCV, with best paper awards. Projects like Face2Face and ScanNet have driven innovation in facial reenactment and 3D scene datasets. Education: PhD in Computer Science, University of Erlangen-Nuremberg (2013) Diploma in Computer Science, University of Erlangen-Nuremberg (2010) Research Interests: 3D digitization, neural rendering, generative AI, non-rigid reconstruction, and applications in AR/VR. Awards: ERC Starting Grant (2018) Nvidia Professorship Award (2018) Google Faculty Award (2018) SIGGRAPH Best Emerging Tech Award (2016) Grants: Over €1.5M from ERC and industry partnerships. Labs/Teams: Visual Computing Lab at TUM and Synthesia Inc. (co-founder). Key projects include ScanNet (large 3D indoor dataset), Face2Face (real-time facial reenactment), and Gaussian-based 3D avatars. Current work focuses on diffusion models, neural radiance fields, and AI-generated media detection.

Anthony RoweView profile

Professor

• Networked Embedded Systems
• Sensor Networks
• Extended Reality (XR) Systems
• +6 more

Anthony Rowe is the Siewiorek and Walker Family Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU) and a Chief Scientist at Bosch Research. His primary affiliation is with the CyLab and the Wireless, Sensing and Embedded Systems (WiSE Lab) at CMU. He specializes in networked embedded systems, sensor networks, and extended reality (XR) technologies. His research emphasizes energy-efficient sensing, real-time localization, and XR integration with physical systems. Research Focus: His work spans XR systems (e.g., AR/VR edge networking in ARENA), mmWave radar for sensing (e.g., tire wear monitoring via Osprey), distributed edge computing (Silverline), and low-power wide-area networking (OpenChirp). Recent efforts include AI-integrated XR platforms (XaiR) and radar tomography (DART). Grants & Projects: Leads the CONIX Research Center ($27.5M NSF/DARPA grant), Bosch-funded edge computing projects, and DOE initiatives on microgrids. Notable projects include ARENA (XR edge architecture), GridBallast (smart grid control), and rural microgrid deployments in Haiti. Awards: Best Student Paper (ISMAR 2024), Best Paper (IPSN 2020), and the Steven J. Fenves Research Award (2015). Recognized for innovations in localization (MobiCom 2021), radar (ICRA 2023), and energy systems (BuildSys 2010). Teaching: Teaches courses on embedded systems (18-349/18-449), real-time systems, and mixed reality (18-453). Courses emphasize hands-on design and real-world applications. Labs & Teams: Directs the WiSE Lab, collaborating with Bosch Research and industry partners. The lab develops open-source frameworks like ARENA and OpenChirp, and contributes to standards for edge computing and sensing.

Haijun XiaView profile

Assistant Professor

• Human-Computer Interaction
• Human-AI Collaboration
• Interface Design
• +2 more

Haijun Xia is an Assistant Professor at the University of California, San Diego (UCSD), where he directs the Foundation Interface Lab and contributes to the Cognitive Science and Design Lab. His work focuses on Human-Computer Interaction (HCI) and Human-AI Collaboration , emphasizing the development of dynamic, malleable interfaces that integrate human cognition with intelligent systems to enhance thinking and working paradigms. Education: Bachelor's, Tsinghua University Master's and PhD, University of Toronto His research spans generative AI , interface design , and creative collaboration , with applications in data visualization, programming, and scholarly work. He advocates for treating information as a malleable material that can be flexibly manipulated by users and AI agents. Recent publications highlight advancements in: 2025 CHI Conference - Generative user interfaces, malleable overview-detail designs, and compositional structures for human-AI co-creation 2024 CHI Conference - Structured design space exploration with LLMs, ASCII diagram analysis, and adaptive presentation systems 2023 CHI/UIST Conferences - Data particle visualization, contextual logging tools, and metaphor generation for science communication Scientific recognitions include: Multiple Best Paper Awards and Honorable Mentions at CHI and UIST Hellman Fellowship and grants from NSF , Microsoft , Google , Meta , Apple , and Adobe He actively recruits undergraduate and graduate interns and has an upcoming postdoc position in early 2025 for researchers interested in foundational interface development.

• Computer Vision
• Object Recognition
• Scene Understanding
• +2 more

Derek W Hoiem is a Professor in the Siebel School for Computing and Data Science at the University of Illinois Urbana-Champaign, where he has been a faculty member since 2009. His research focuses on computer vision and related areas, and he is also the co-founder and Chief Science Officer of Reconstruct, an AI-based construction technology company. His educational background includes: PhD in Robotics, Carnegie Mellon University (2007) Beckman Postdoctoral Fellowship (2008) Prof. Hoiem's research spans computer vision, with a focus on object recognition, scene understanding, and graphics. His work also extends to mobile robotics and 3D scene reconstruction. He has made significant contributions in areas such as visual recognition, 3D modeling, and the application of computer vision in construction monitoring. His recent publications (2023-2025) demonstrate a strong focus on advancing multimodal understanding, particularly in region-based representations, 3D vision, and neural radiance fields. There is a clear trend towards integrating language and vision, improving efficiency in neural networks, and applying computer vision to real-world problems such as construction progress monitoring. His scientific awards and honors are extensive and include: IEEE Fellow (2022) University Scholar (2022) Koendrink Prize (2022) Dean's Award for Excellence in Research, Associate Professor (2021) Campus Distinguished Promotion Award (2015) Best Paper Award: IEEE Winter Conference on Applications in Computer Vision (WACV) (2015) CW Gear Junior Faculty Award (2014) IEEE PAMI Young Researcher Award (2014) Dean's Award for Excellence in Research, Assistant Professor (2014) Sloan Research Fellowship (2013) Intel Early Career Faculty Honor Program Award (2012) NSF CAREER Award (2011) ACM Doctoral Dissertation Award, Honorable Mention (2008) Carnegie Mellon University SCS Distinguished Dissertation Award (2008) Best Paper Award: IEEE Computer Vision and Pattern Recognition (CVPR) (2006) Prof. Hoiem has secured significant research funding, including an NSF CAREER award and an Intel Early Career Faculty award. He is also actively involved in technology transfer, having co-founded Reconstruct where he serves as Chief Science Officer. His teaching excellence is reflected in multiple "List of Teachers Ranked as Excellent" awards spanning from 2010 to 2021. Prof. Hoiem leads a research group at UIUC focused on computer vision and 3D scene understanding. Additionally, he co-founded and serves as Chief Science Officer at Reconstruct, which develops AI-based solutions for construction monitoring.

Hyesoon KimView profile

Professor

• Computer Architecture
• GPU
• Compilers and Runtime Systems
• +3 more

Hyesoon Kim is a Professor at the Georgia Institute of Technology , affiliated with the College of Computing and leading the HPArch research group . She co-directs the Center for Research into Novel Computing Hierarchies (CRNCH) . Her research focuses on Computer Architecture , GPU , Compilers and Runtime Systems , and Hardware Security , particularly for heterogeneous systems. Contact : hyesoon@cc.gatech.edu Location : 266 Ferst Drive, KACB 2344, Atlanta, GA Research Trends Her recent work spans RISC-V extensions for security, CUDA optimization on softcore GPUs, memory safety techniques, and energy-efficient deep learning architectures. Articles emphasize heterogeneous computing , GPU performance scaling, and IoT -oriented neural network methods. Open Source Projects She leads development of Macsim (heterogeneous architecture simulator) and Vortex (open-source GPU platform).

Xingang PanView profile

Assistant Professor

• Computer Vision
• Generative Models
• 3D Vision
• +3 more

Xingang Pan is an Assistant Professor in the College of Computing and Data Science at Nanyang Technological University (NTU), leading the MMLab@NTU. His research focuses on generative AI and visual content creation, particularly in generative models, 3D vision, computer graphics, and computer vision. Prior to NTU, he was a postdoc at the Max Planck Institute for Informatics and earned his Ph.D. from the Chinese University of Hong Kong (2021) and B.Sc. from Tsinghua University (2016). His work emphasizes generative intelligence, exploring long-term world simulation, diffusion models, and multi-scale 3D generation. Notable contributions include WORLDMEM (2025), Alias-free Latent Diffusion (2025), and SAR3D (2025). His research has been published in top venues like CVPR, ICCV, and SIGGRAPH. Xingang Pan oversees the MMLab@NTU, which actively recruits students globally without nationality constraints. The lab’s projects include GAN2Shape (unsupervised 3D reconstruction from 2D GANs) and LN3Diff (scalable 3D generation).

Jeff HuangView profile

Associate Professor

• Human-Computer Interaction
• Data Science
• Health Informatics (eHealth/mHealth)
• +3 more

Jeff Huang is an Associate Professor and Associate Chair of Computer Science at Brown University. His research focuses on Human-Computer Interaction (HCI), with emphases on personalized systems using behavioral data, health informatics, and user interfaces. Huang holds a PhD from the University of Washington and degrees from the University of Illinois at Urbana-Champaign. His work is funded by NSF, NIH, and ARO, and he has received awards including the NSF CAREER Award and ARO Young Investigator Award. Education: PhD in Information Science, University of Washington (2013) MS in Computer Science, University of Washington (2013) BS and MS in Computer Science, University of Illinois at Urbana-Champaign (2007) Research Interests: His work spans health informatics (e.g., sleep tracking systems like Self-E and SleepCoacher), user interface design, and leveraging behavioral data for personalized systems. Notable projects include WebGazer (webcam eye tracking), filtered.ink (creative illustration tools), and Sochiatrist (mental health analytics). Teaching: He teaches courses on user interfaces, HCI, and research methods, including CSCI 0130/1300 (User Interfaces), CSCI 2300 (HCI Seminar), and CSCI 2000 (Research Methods). Awards: NSF CAREER Award Facebook Fellowship ARO Young Investigator Award Grants & Advising: Huang leads a research group advising over 50 students, many of whom have pursued academic and industry roles. His grants support projects in HCI, health tech, and collaborative systems.

Wojciech MatusikView profile

Professor

• Computer Graphics
• Computational Design and Fabrication
• Computer Vision
• +7 more

Wojciech Matusik is a Professor of Electrical Engineering and Computer Science at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). He leads the Computational Design and Fabrication Group and is a member of the Computer Graphics Group. His research spans computer graphics, robotics, and AI-driven manufacturing, with a focus on computational design, tactile sensing, and material science. Matusik holds a PhD in Computer Science from MIT (2003), an MS from MIT (2001), and a BS from UC Berkeley (1997). His work includes groundbreaking projects like differentiable cloth simulation (DiffCloth), AI-enhanced molecular design, and tactile sensing gloves. He has received prestigious awards such as the MIT TR35 (2004), DARPA Young Faculty Award (2012), and Ruth and Joel Spira Teaching Award (2014). Matusik teaches courses on computer graphics, machine learning, and computational fabrication at MIT. Key research themes include: Robotics: Robotic assembly, tactile interaction, and soft robotics Graphics: 3D holography, procedural material generation Manufacturing: Additive fabrication, topology optimization His recent articles explore AI-driven molecular synthesis, holographic displays, and tactile-enabled VR systems. Matusik collaborates on open-source tools like the WiReSens tactile platform and Simit language for sparse systems.

Maria GorlatovaView profile

Associate Professor

• Augmented Reality
• Virtual Reality
• Internet of Things
• +7 more

Maria Gorlatova is an Associate Professor of Electrical and Computer Engineering at Duke University's Pratt School of Engineering, where she leads the Intelligent Interactive Internet of Things (I3T) Lab. She also holds a secondary affiliation as Faculty Network Member of the Duke Institute for Brain Sciences and has previously served as Assistant Professor of Computer Science. Dr. Gorlatova earned her Ph.D. in Electrical Engineering from Columbia University (2013), following M.Sc. and B.Sc. (Summa Cum Laude) degrees in Electrical Engineering from University of Ottawa, Canada. Prior to joining Duke, she was an Associate Research Scholar in the Electrical Engineering Department and Associate Director of the Princeton EDGE Lab at Princeton University (2016-2018). She also has industry experience with Telcordia Technologies, IBM, and D. E. Shaw Research. Her research focuses on advancing intelligent behavior in Internet of Things systems and applications, particularly in mobile pervasive systems and the Internet of Things. Her work crosses traditional discipline boundaries, requiring thinking across multiple layers of system and protocol stacks. Current research themes include breaking barriers for technologies that enable fundamentally new deployments and experiences, such as energy harvesting, artificial intelligence adapted to IoT constraints, and augmented reality. Her lab specifically develops edge- and IoT-enabled intelligent augmented reality platforms, with applications in healthcare and human-robot collaboration. Analyzing her recent publications reveals a strong focus on augmented reality systems, particularly for medical applications. Her work spans computer vision for AR, spatial tracking, SLAM systems, vision-language models for AR security, and VR/AR applications in neurosurgery and rehabilitation. A significant portion of her recent work addresses challenges in mixed reality for medical procedures, demonstrating the translational impact of her research. Google Anita Borg USA Fellowship Canadian Graduate Scholar CGS NSERC Fellowships Columbia University Presidential Fellowship Columbia University Jury Award for Outstanding Achievement in Communications ACM SenSys Best Student Demonstration Award IEEE Communications Society Young Author Best Paper Award IEEE Communications Society Award for Advances in Communications Best Research Artifact Award, IEEE IPSN (2020) N2 Women Rising Star, Networking Networking Women (N2Women) (2019) Dr. Gorlatova's research has been supported by various funding sources that enable her work on edge computing for augmented reality, IoT systems, and medical applications. She actively mentors graduate students who frequently appear as first authors on her publications, indicating strong student involvement in her research. Her I3T Lab at Duke focuses on creating human-facing pervasive mobile computing platforms that enable transformative applications, with recent emphasis on creating advanced augmented reality platforms that integrate edge computing and IoT technologies. The I3T Lab is developing next-generation AR systems with capabilities in edge AI, collaborative spatial awareness, AR user cognitive context sensing, and AR QoS/QoE evaluation. Current projects include applications in healthcare (particularly neurosurgery guidance and rehabilitation) and human-robot collaboration scenarios, demonstrating the lab's focus on real-world impact of pervasive computing technologies.

Ajit RajwadeView profile

Professor

• Artificial Intelligence
• Machine Learning
• Signal Processing
• +6 more

Ajit Rajwade is a Professor at the Department of Computer Science and Engineering , Indian Institute of Technology Bombay. His research spans Artificial Intelligence , Compressed Sensing , and Medical Imaging , with affiliations to the Centre for Machine Intelligence and Data Science and the Koita Centre for Digital Health . Education : PhD in Computer and Information Science and Engineering, University of Florida (2010) MSc in Computer Science, McGill University (2004) BTech in Computer Engineering, University of Pune (2002) His research interests focus on intelligent data acquisition, particularly in neural network analysis , graph signal processing , and medical imaging . He develops compressed sensing algorithms for inverse problems like tomography and MRI reconstruction , alongside applying group testing to pandemic response. Scientific awards include the Prof. S. P. Sukhatme Award (2024) and Departmental Teaching Excellence (2019). His publications demonstrate expertise in image restoration , noise modeling , and epidemiological algorithms . He has advised PhD students like Sabyasachi Ghosh and Jerin Geo James , with a focus on computationally efficient methods in medical imaging and machine learning .

Rana HanockaView profile

Assistant Professor

• Computer Graphics
• Machine Learning
• Computer Vision
• +3 more

Rana Hanocka is an Assistant Professor of Computer Science at the University of Chicago, leading the 3DL research group focused on AI-driven 3D geometry processing. She earned her Ph.D. in 2021 from Tel Aviv University under Professors Daniel Cohen-Or and Raja Giryes. Her work explores neural networks for unstructured 3D data, including mesh convolutional networks and self-priors for shape reconstruction. Key research areas include geometric deep learning, human-AI collaboration in 3D modeling, and interpretable neural networks. Notable contributions include MeshCNN (SIGGRAPH 2019) and Point2Mesh (NeurIPS 2020), advancing mesh analysis and point cloud processing. Awards include the 2023 Pazy Research Award and 2020 Rising Star in EECS. Education : Ph.D. Computer Science, Tel Aviv University (2021) Labs/Groups : 3DL Group at UChicago Grants : NSF Grant for AI-driven 3D modeling tools (2023) Research directions emphasize creative human-AI partnerships, unsupervised learning from shape collections, and explainable 3D neural networks. Ongoing projects include style-aware 3D synthesis, interactive segmentation, and multimodal shape interfaces.

Federico ZANETTINView profile

Professor

• Corpus Linguistics
• Translation Studies
• Graphic Narrative Translation
• +5 more

Federico ZANETTIN is a Full Professor in the Department of Comparative Linguistic and Cultural Studies at Ca' Foscari University of Venice. His research focuses on translation studies with particular emphasis on corpus linguistics, graphic narrative translation, and journalistic translation. He maintains an active presence in the academic community through editorial work and research collaborations. Professor ZANETTIN's research interests span multiple areas of translation studies. He is particularly known for his work on corpus linguistics and its applications to translation analysis, with a focus on creating and using linguistic databases. His research on the translation of graphic narratives examines the verbal and visual aspects of translating comics and graphic novels across cultures. Additionally, he investigates journalistic translation , analyzing how information is translated and disseminated across media platforms. His scholarly work demonstrates consistent productivity, with numerous publications that explore the intersection of translation, culture, and media. Professor ZANETTIN's research shows particular strength in analyzing adaptation processes across linguistic, cultural, and modal boundaries, both synchronically and diachronically. Professor ZANETTIN serves on several important editorial boards: Co-editor of inTRAlinea Member of the international advisory board for The Translator Member of the international advisory board for Translang Member of the international advisory board for Entreculturas Member of the publications committee for Transletters. International Journal of Translators and Interpreters Member of the scientific committee for the Book series Interactio He is also frequently called upon as a referee for prestigious research projects, including those funded by The British Academy (UK), MIUR (Ministry of Education, University and Research), Italy, CSIC (Consejo Superior de Investigaciones Científicas), Spain, NRC (National Research Council), Canada, National and Kapodistrian University of Athens, Greece, and SNSF (Swiss National Science Foundation). Professor ZANETTIN teaches courses in English language and translation at both undergraduate and graduate levels, focusing on European, American, and postcolonial languages and literatures. His teaching reflects his research expertise in translation studies and cross-cultural communication.

Olga Sorkine HornungView profile

Professor

• Computer Graphics
• Geometric Modeling
• Geometry Processing
• +3 more

Prof. Olga Sorkine Hornung is a Full Professor of Computer Science at ETH Zürich, leading the Interactive Geometry Lab. She holds a BSc and PhD from Tel Aviv University (2000 and 2006) and conducted postdoctoral research at Technical University Berlin. Her research focuses on computer graphics, geometric modeling, and geometry processing, with applications in shape editing, digital fabrication, and animation. She has received numerous accolades, including the ACM Fellowship (2020), ERC Consolidator Grant (2020), and the Golden Owl Teaching Award (2021). Her work bridges theoretical foundations and practical algorithms, addressing challenges in parameterization, surface compression, and interactive design tools. Her research interests span: Computer Graphics & Visualization Geometric Modeling & Processing 3D Content Creation & Digital Fabrication Garment Design & Simulation Human Motion Analysis & Animation Awards and grants include: 2024: Best Paper Honorable Mention (EUROGRAPHICS) 2023: Member of Swiss Academy of Engineering Sciences (SATW) 2020: ERC Consolidator Grant 2017: Rössler Prize (ETH Zurich) Her lab focuses on developing novel methods for interactive geometry processing, with recent advancements in garment modeling (e.g., AIpparel, Rags2Riches) and motion retargeting systems like WalkTheDog. She actively collaborates on interdisciplinary projects, including biomedical applications and sustainable fashion technology.

Eytan AdarView profile

Professor

• Human-Computer Interaction
• Artificial Intelligence
• Data Mining
• +7 more

Eytan Adar is a Professor of Information and Computer Science at the University of Michigan, holding dual appointments in the School of Information and the College of Engineering's Electrical Engineering and Computer Science department. His work sits at the intersection of human-computer interaction and artificial intelligence, focusing on large-scale systems analysis and novel interface design. Adar's research spans multiple domains including social media analysis (Twitter, Reddit), academic citation networks, meme propagation, information extraction, and political networks. His methodological expertise includes data mining, data visualization, and graph-based network analysis. His work often operates at internet scale, examining language, creativity, social network dynamics, and text production. His recent publications reveal a strong focus on AI-human collaboration, with particular attention to generative AI interfaces, visualization techniques for complex data, and ethical considerations in AI systems. His work shows a consistent pattern of bridging theoretical insights with practical system implementations. Best Paper Award at ProtoAI: Model-Informed Prototyping for AI-Powered Interfaces, IUI'21 Honorable Mention Award at CHI'24 for feminist interaction techniques research Best Paper Award at ICWSM'18 for Wikipedia language edition analysis Best Paper Award at ICML 2016 Workshop for neural language model visualization Best Student Paper at WSDM'09 for web dynamics research Best of CHI at CHI 2008 for web revisitation patterns analysis Adar has advised numerous PhD students who have gone on to prominent positions at organizations including Google, Apple, RAND, Northwestern University, and Stanford. His research is generously supported by the NSF, IARPA, NIH, the Education Department, and major technology companies including Adobe, Microsoft, Facebook, Google, and Yahoo. He is also a founder of the International Conference on Web and Social Media (ICWSM) and has served as Co-General Chair for WSDM and Co-Program Chair for UIST.