جستجوی استاد راهنمای دانشگاهی

Gierad Laputمشاهده پروفایل

استاد مدعو

• Human-Computer Interaction
• Machine Learning
• Sensing Systems
• +۷ مورد دیگر

Gierad Laput is a Senior Engineering Manager at Apple, leading the Perception and Interaction Engineering organization under Machine Intelligence Neural Design (MIND) at Apple AI/ML (AIML). He is also an Adjunct Professor at Carnegie Mellon University's School of Computer Science and Human-Computer Interaction Institute. His research sits at the intersection of sensing systems, interactive technologies, and machine learning, with applications in ambient intelligence, wearables, and AI-driven interactive experiences. Ph.D. in Computer Science from Carnegie Mellon University Founding member of CMU's Future Interfaces Group Former Google Ph.D. Fellow, Disney Imagineer, and recipient of 10 Best Paper Awards/Nominations His research agenda at Apple leverages cutting-edge hardware to develop multimodal ML systems for vision, audio, and motion sensors, enabling features like head gesture detection for AirPods, Apple Intelligence integrations, and health monitoring tools. Recent collaborative projects include Vision Pro hand interaction systems and WatchOS updates for cycling/fall detection. Key research areas include human-computer interaction , machine learning for sensor fusion , and smart environment sensing . Over 35 publications cover transformative technologies like ViBand (bio-acoustic sensing), EM-Sense (touch recognition), SkinTrack (arm as touchpad), and ElectroDermis (stretchable electronics). Scientific honors include: Fast Company Innovation by Design Award (2017) Google PhD Fellowship (2018) Swartz Entrepreneurial Fellowship (2018) Best Paper Awards at ACM UIST (2016) and ACM CHI (2015) As an educator, he has taught courses at CMU and the University of Michigan on on-device machine learning , signal processing , and human-centered design . His lab has mentored numerous researchers now pursuing PhDs at MIT, UW, CMU, and Northwestern.

• Speech and Audio Processing
• Behavioral Signal Processing
• Real-Time MRI of Speech Production
• +۴ مورد دیگر

Shrikanth (Shri) Narayanan is a University Professor and holder of the Niki and Max Nikias Chair in Engineering at the University of Southern California (USC), serving as the inaugural Vice President for Presidential Initiatives. He leads the Signal Analysis and Interpretation Lab (SAIL) and holds joint appointments in Computer Science, Linguistics, Psychology, Neuroscience, Pediatrics, and Otolaryngology-Head and Neck Surgery. His research focuses on speech and audio processing, behavioral signal processing, and real-time MRI of speech production, with applications in healthcare, education, and technology. Education: B.E. in Electrical Engineering from College of Engineering, Guindy (Chennai, India, 1988); M.S., Engineer, and Ph.D. in Electrical Engineering from UCLA (1990, 1992, 1995). Research interests span computational linguistics, machine learning, and multimodal human behavior analysis. He pioneered technologies for speech biomarkers in mental health, real-time MRI of speech production, and wearable sensor systems for longitudinal health studies. His work in speech emotion recognition, forensic interviews, and clinical applications has been recognized through over 40 awards, including the IEEE Flanagan Award and ISCA Medal. He has published extensively in journals like Proceedings of the IEEE , Journal of the Acoustical Society of America , and PLOS One . Key Grants: NSF CAREER, Okawa Research, IBM Faculty, Google/Amazon awards. Labs/Teams: Signal Analysis & Interpretation Lab (SAIL), USC Information Sciences Institute (ISI), Google Visiting Faculty Researcher.

Chua Tat Sengمشاهده پروفایل

استاد

• unstructured data analytics
• multimedia information retrieval
• recommendation systems
• +۶ مورد دیگر

Chua Tat Seng is a Professor at the School of Computing, National University of Singapore (NUS), holding the KITHCT Chair Professorship since 2009. He serves as co-Director of the NExT++ Center, a joint research center between NUS and Tsinghua University focused on Extreme Search. His academic career spans over three decades at NUS, where he has held various leadership positions including Acting Dean of the School of Computing (1998-2000) and Acting Head of the Department of Information Systems & Computer Science (1996-1998). Professor Chua's research spans unstructured data analytics , multimedia information retrieval , recommendation and conversation systems , and emerging applications in e-commerce and fintech . He established the Lab for Media Search (LMS) at the School of Computing and has been instrumental in advancing multimodal learning and search technologies. His work bridges theoretical foundations with practical applications, particularly in developing trustable AI systems for real-world deployment. His recent publications demonstrate a strong focus on large language models for recommendation systems , multimodal learning , and generative AI applications . The research trends show increasing emphasis on LLM-based recommendation, multimodal understanding, and addressing fundamental challenges in AI reliability, fairness, and efficiency. His work spans theoretical advancements in representation learning to practical applications in e-commerce, finance, and healthcare domains. ACM SIGMM Technical Achievement Award 2015 Multiple Best Paper Awards across ACM Multimedia, IEEE Transactions, and MMM conferences (2007-2020) Professor Chua has supervised 37 PhD students since 2004, establishing himself as a dedicated mentor in the academic community. His research has been supported by substantial grants including NExT++ ($12 million), Base Metals Price Forecasting ($200,000), and Multilingual Multimodal Knowledge Graph ($500,000). He maintains active collaborations with Tsinghua University, University of Southampton, and industry partners like Four Elements Capital and Singapore Press Holdings. As co-Director of the NExT++ Center, he leads a major research initiative focused on Web Intelligence and User Empowerment. His visiting professorships at Tsinghua University (2017-present) and Zhejiang University (2021-present) reflect his international impact in the field of multimedia and AI research.

• Human-Computer Interaction
• Learning Sciences
• Educational Technology
• +۲ مورد دیگر

Jennifer Olsen, PhD, is an Assistant Professor of Computer Science at the University of San Diego since 2020. She holds a PhD, MS, and BS in Human-Computer Interaction and Cognitive Science from Carnegie Mellon University, followed by postdoctoral research at the Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland. Her research focuses on the intersection of human-computer interaction, cognition, and education, emphasizing collaborative learning and educational technology design from both learner and instructor perspectives. Education: PhD in Human-Computer Interaction, Carnegie Mellon University MS in Human-Computer Interaction, Carnegie Mellon University BS in Cognitive Science, Carnegie Mellon University Research Interests: Dr. Olsen explores how collaboration supports learning, designs technologies to enhance educational practices, and investigates gaze-based metrics for understanding collaborative problem-solving. Her work spans gamified robotics, AI-driven orchestration systems, and virtual reality applications in vocational training. She emphasizes learner-centered design and the integration of social robots and virtual agents in pedagogical settings. Grants/Advising: While no specific grants or advisees are listed, her prolific publication record indicates active involvement in educational technology research and development. Her work addresses challenges in classroom orchestration, multimodal data analysis, and accessibility in educational robotics. Labs/Teams: Collaborates with interdisciplinary teams focused on educational technology, human-robot interaction, and adaptive learning systems. Her research leverages tools like FROG orchestration graphs and eye-tracking technologies to develop practical classroom solutions.

David Bammanمشاهده پروفایل

دانشیار

• Natural Language Processing
• Cultural Analytics
• Computational Social Science
• +۳ مورد دیگر

David Bamman is an Associate Professor in the School of Information at UC Berkeley, specializing in applying Natural Language Processing (NLP) and machine learning to cultural and social science questions. He leads research in born-literary NLP, computational humanities, and cultural analytics, with affiliated roles in EECS, Linguistics, and Computational Precision Health. Bamman holds degrees from Carnegie Mellon (Ph.D., 2015), Boston University (M.A., 2006), and University of Wisconsin-Madison (B.A., 1998). His work is supported by NEH, NSF, and industry grants. Educations: Ph.D. in Computer Science (2015), Carnegie Mellon University M.A. in Applied Linguistics (2006), Boston University B.A. in Classics (1998), University of Wisconsin-Madison Research Interests: NLP for underserved domains (e.g., literature, social media), coreference resolution, cultural analytics, and computational methods for studying literature and culture. Projects include LitBank and BookNLP datasets. Grants & Awards: Hellman Fellow (2019), Amazon Research Award (2017), NSF CAREER Award, and NEH funding. Teaching: Courses include Natural Language Processing (Info 159/259), Computational Humanities (INFO 190), and Applied NLP (INFO 256). His research group explores topics like racial representation in high school literature, Hollywood diversity metrics, and the sociocultural implications of LLMs. Bamman advises multiple PhD students and collaborates on datasets like CMU Book Summaries and 11K Latin Books.

Tien Tsin Wongمشاهده پروفایل

استاد

• Generative AI
• Computer Graphics
• Computer Vision
• +۴ مورد دیگر

Tien Tsin Wong is a Professor in the Department of Data Science & AI at Monash University, Australia. Previously, he served as a Professor at the Chinese University of Hong Kong (1999–2024) and held a Visiting Assistant Professor position at the Hong Kong University of Science and Technology (1998–1999). His research focuses on Generative AI, Computer Graphics, Computer Vision, and Computational Manga, with significant contributions to GPU techniques, image-based rendering, and multimedia compression. Education: He earned a B.Sc. (1992), MPhil (1994), and PhD (1998) in Computer Science from the Chinese University of Hong Kong. Research Interests: His work bridges computational techniques with artistic applications, particularly in manga and animation. Notable areas include generative models, diffusion-based video synthesis, and physically plausible scene generation. His research aligns with UN Sustainable Development Goals through innovations in education and digital accessibility. Awards : He has received the 2004 Young Researcher Award, 2005 IEEE Transactions on Multimedia Prize Paper Award, and two international invention medals (Geneva 2018, Asia Hong Kong 2019). Editorial Roles : He serves as an Associate Editor for Computer Graphics Forum , IEEE Transactions on Visualization and Computer Graphics , and Computational Visual Media . His editorial work underscores his influence in advancing visualization and graphics research. Labs/Teams : While not explicitly named, his collaborations span global institutions, focusing on computational manga, generative AI, and GPU-optimized techniques. His work often involves interdisciplinary teams addressing challenges in digital media and AI.

Adriana Kovashkaمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Natural Language Processing
• +۷ مورد دیگر

Adriana Kovashka is an Associate Professor at the University of Pittsburgh , affiliated with the School of Computing and Information and serving as Department Chair . Her academic journey began with BA degrees in Computer Science and Media Studies from Pomona College (2008) and a PhD in Computer Science from The University of Texas at Austin (2014). Joined Pitt’s faculty in January 2015 NSF CAREER awardee (2021) Google Faculty Research Award recipient Dr. Kovashka’s research spans Computer Vision , Machine Learning , and Natural Language Processing , focusing on visual rhetoric, weak multimodal supervision, and domain adaptation. She pioneered techniques for analyzing political imagery, developing robust object detection frameworks, and exploring the intersection of visual and textual persuasion through large-scale annotated datasets. Her recent work emphasizes geographic diversity in vision-language systems, audio-visual fusion for domain generalization, and shape-texture bias mitigation in CNNs. Key publications include groundbreaking studies on symbolic reasoning, multimodal dialogue systems, and ethical AI applications in education. Scientific honors include: NSF CRII Award (2016) NSF CAREER Award (2021) Pitt CRDF Award (2016, 2018) Best Paper at ECV Workshop (2021) Google Faculty Research Award (2016, 2018) Dr. Kovashka actively mentors students in multimodal learning projects and collaborates with interdisciplinary teams on NSF-funded initiatives. She co-organizes workshops like the first CVPR workshop on advertisement understanding and leads research groups exploring human-AI co-learning systems.

Alan Ritterمشاهده پروفایل

دانشیار

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Alan Ritter is an Associate Professor at the School of Interactive Computing , Georgia Institute of Technology, with additional affiliation to the Machine Learning Center . His research focuses on Natural Language Processing , particularly robust models across domains/languages with fewer labels and efficient resource use, plus data-driven dialogue agents for open-topic conversations. Research Interests : Robust NLP models, cross-lingual transfer, resource-efficient learning, dialogue systems, cultural bias measurement, and privacy-aware language models Students : Mentors Ph.D. students in Georgia Tech's ML and CS programs, including Junmo Kang, Yang Chen, and Duong Minh Le. Alumni include Fan Bai (Ph.D. 2023), Yang Chen (Ph.D. 2024), and Andrew Li (M.S. 2024). Awards : NSF CAREER Award, Amazon Research Award, ACL 2024 Best Social Impact Paper, IUI 2009 Best Student Paper. Recent Work : Studies training budget allocation between supervised and preference-based finetuning, cross-lingual information extraction, cultural bias in LLMs, and privacy risk mitigation in social media disclosures. Service : Served as Program Chair for NAACL 2025, Area Chair for multiple top-tier conferences (COLM, EMNLP, ACL, EACL, AAAI). Email : alan.ritter@cc.gatech.edu

Hanjie Chenمشاهده پروفایل

استادیار

• Natural Language Processing
• Interpretable Machine Learning
• Trustworthy AI
• +۵ مورد دیگر

Hanjie Chen is an Assistant Professor in the Department of Computer Science at Rice University, affiliated with the Ken Kennedy Institute. She holds a Ph.D. from the University of Virginia and a Master's from the University of Science and Technology of China. Her research focuses on Natural Language Processing, Interpretable Machine Learning, and Trustworthy AI, emphasizing model explainability, alignment with human needs, and applications in healthcare, sports, and medicine. She has advised numerous students and led initiatives in AI ethics and education. Education: Ph.D. (Computer Science, UVA 2023), M.Sc. (USTC 2018), B.Sc. (Nanjing University of Aeronautics and Astronautics 2015). Awards include the Outstanding Doctoral Student Award (UVA 2023) and John A. Stankovic Research Award (UVA 2023). She has organized workshops like BlackboxNLP and served on program committees for ACL, NAACL, and EMNLP. Her recent work includes developing benchmarks like SPORTU for multimodal LLMs, evaluating medical question-answering systems, and advancing methods for robust rationale evaluation (RORA). She teaches courses on Natural Language Processing and Trustworthy NLP, emphasizing pedagogical innovation recognized by teaching awards at UVA. Research collaborations include internships at Microsoft Research, IBM, and the Allen Institute for AI. She mentors students in SURF programs and advocates for diversity in tech, serving as a mentor in UVA's CSGSG Council.

Bobak Mortazaviمشاهده پروفایل

دانشیار

• Medical analytics
• Machine learning
• Embedded systems design
• +۳ مورد دیگر

Bobak Mortazavi is an Associate Professor in the Department of Computer Science & Engineering at Texas A&M University. His research focuses on medical analytics, machine learning, wearable sensors, and cyber-physical systems. He leads interdisciplinary projects in healthcare technology, including AI-driven diagnostics and predictive modeling for cardiovascular diseases. He has received notable awards such as the Best Demonstration Award at IEEE EMBS 2012 and the Best Paper Award at the Fourth International Conference on Data Analytics 2015. His work bridges machine learning with clinical applications, emphasizing practical solutions for healthcare challenges. Recent research includes developing AI tools for aortic stenosis detection, electrolyte estimation via ECG, and real-time patient monitoring systems. He collaborates with industry and academic partners to advance telemedicine and wearable health technologies. Key contributions include the SMART-LV project for smartphone-based cardiac diagnostics and the ArterialNet framework for blood pressure reconstruction using wearable sensors. His work is published in top journals like IEEE Journal of Biomedical and Health Informatics and Elsevier's Pervasive and Mobile Computing.

Haijun Xiaمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Human-AI Collaboration
• Interface Design
• +۲ مورد دیگر

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.

Jimeng Sunمشاهده پروفایل

استاد

• Artificial Intelligence
• Healthcare Analytics
• Drug Discovery
• +۵ مورد دیگر

Dr. Jimeng Sun is a Health Innovation Professor at the Siebel School of Computing and Data Science and Carle Illinois College of Medicine at the University of Illinois Urbana-Champaign. Co-founder of Keiji AI , he leads groundbreaking research at the intersection of artificial intelligence and healthcare, actively deploying clinical AI systems and developing frameworks like PyHealth and Therapeutics Data Commons . His research spans four major areas: Clinical AI Systems : Developing interpretable models (e.g., RETAIN) for patient similarity, temporal event prediction, medication recommendation, and clinical outcome forecasting Drug Discovery : Creating molecular optimization frameworks, drug-target interaction models, and AI-driven platforms Clinical Trials : Pioneering patient-trial matching, outcome prediction, and optimization frameworks using deep learning and graph neural networks Biosignal Analysis : Advancing sleep staging, seizure classification, and automated EEG/Cardiac monitoring systems With over 500 top-tier publications (including in Nature , NEJM AI , and leading AI conferences) and an h-index of 99, his work has been recognized with the Top 100 AI Leaders in Drug Discovery and Advanced Healthcare award. He maintains active collaborations with institutions like Massachusetts General Hospital , Medidata Solutions , and OSF Healthcare . His recent publications reveal a strong focus on: Reinforcement learning applications in medical data analysis Large language model adaptation for clinical tasks Knowledge graph integration with AI systems Synthetic data generation for healthcare Multi-modal learning in clinical contexts Explainable AI for medical applications Dr. Sun's lab ( Sunlab ) emphasizes practical impact over theoretical work, actively collaborating with hospitals and healthtech companies. He welcomes contributions from clinicians, researchers, and industry partners through initiatives like his AI for Health webinar series .

• Computer Vision
• Machine Learning
• Autonomous Driving
• +۴ مورد دیگر

Prof. Laura Leal-Taixé is a Professor at the Technical University of Munich (TUM) in the Department of Informatics, leading the Dynamic Vision and Learning group. She holds the Rudolf Mößbauer Tenure Track Assistant Professorship, promoted to W3 Associate Professorship in 2022. Her research focuses on computer vision and machine learning, particularly video analysis, multi-object tracking, and autonomous driving applications. Education: B.Sc./M.Sc. in Telecommunications Engineering (Technical University of Catalonia, UPC) Ph.D. in Information Processing (Leibniz University of Hannover, Germany) Postdoctoral Research at ETH Zürich (Switzerland) and TUM Research Interests: Laura’s work addresses challenges in video analysis, including motion analysis, semantic segmentation, and integrating social dynamics into urban traffic modeling. Her project socialMaps , funded by the Sofja Kovalevskaja Award, explores decoupling vehicle and pedestrian traffic using dynamic maps. Her research combines optimization techniques, deep learning, and sensor data for real-world applications like autonomous systems. Recent Trends: Her publications highlight advancements in multi-object tracking, 3D LiDAR segmentation, and trajectory forecasting, emphasizing neural networks and geometric approaches. Collaborations with industry and academia underscore her work’s practical impact. Awards: 2017 Sofja Kovalevskaja Award (€1.65M, Humboldt Foundation) 2017 DAAD Australia-German Joint Research Grant Travel grants from Women in CV, CVPR Doctoral Consortium, and Vodafone Foundation Labs & Projects: Leads the Dynamic Vision and Learning group at TUM, focusing on vision-based AI for autonomous systems and environmental monitoring. Active in developing datasets like DynamicEarthNet for semantic change analysis.

Andreas Bullingمشاهده پروفایل

استاد

• Human-Computer Interaction
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Andreas Bulling is a Professor at the Institute for Visualisation and Interactive Systems , University of Stuttgart, Germany. His research focuses on Human-Computer Interaction , Eye Tracking , and Computer Vision , with applications in Machine Learning , Virtual Reality , and Information Visualization . 2025 Publications: HOIGaze (Extended Reality), ChartQC (Data Visualization), HAIFAI (Human-AI Interaction), SummAct (Behavioral Summarization), Chartist (Chart Reading). 2024 Contributions: HumanEYEze (Multimodal AI), HOIMotion (3D Object Detection), MultiMediate'24 (Engagement Estimation), Unified Model of Saliency (Scanpath Prediction). His recent work explores gaze estimation , interactive behavior modeling , and privacy-preserving eye-tracking systems . Key subfields include Extended Reality , Neural Networks , and Behavioral Biometrics . While no explicit scientific awards are mentioned, his research has been widely cited (8,003 total citations) and downloaded (132,740 times). Andreas leads projects in Interactive Systems and collaborates with institutions such as Aalto University , KU Leuven , and National University of Singapore . His lab focuses on eye movement analysis , human motion forecasting , and task-driven input modeling .

Kamalika Chaudhuriمشاهده پروفایل

استاد

• Machine Learning
• Trustworthy Machine Learning
• Differential Privacy
• +۴ مورد دیگر

Kamalika Chaudhuri is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego (UCSD), and also serves as a Director and Research Scientist with the FAIR team at Meta AI. Her research focuses on the foundations of trustworthy machine learning, including robust machine learning, learning with privacy, and out-of-distribution generalization. Dr. Chaudhuri has earned her PhD from UC Berkeley in 2007 with a dissertation on "Learning Mixtures of Distributions." Her academic journey has led her to become a leading researcher in machine learning theory with a particular emphasis on privacy and robustness. Her research interests span machine learning foundations with a strong focus on trustworthy AI . She investigates problems at the intersection of differential privacy , adversarial robustness , and out-of-distribution generalization . Her work addresses critical challenges in developing machine learning systems that maintain privacy while preserving utility, resist adversarial attacks, and generalize effectively beyond training data distributions. She has pioneered approaches in privacy-preserving machine learning, robust learning theory, and methods for detecting and mitigating data memorization in models. An analysis of her recent publications (2024-2025) reveals a strong focus on the intersection of privacy, security, and machine learning. Her work spans differential privacy mechanisms, membership inference attacks, memorization detection, and fairness certification. She has been particularly active in developing methods for privacy-preserving foundation models, with several papers on differentially private computer vision and language models. Her research demonstrates a consistent thread of addressing fundamental challenges in trustworthy AI while developing practical solutions that balance privacy, accuracy, and utility. Best Award at the ICLR 2024 Workshop on Privacy Regulation and Protection in Machine Learning Distinguished Paper Award at IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 2024 Dr. Chaudhuri has advised numerous PhD students who have gone on to prominent positions at Google, DeepMind, Microsoft Research, and other leading AI institutions. Her group maintains an active research blog with guest posts from UCSD researchers. She has served in significant leadership roles including General Chair for ICML 2022 and Program Co-Chair for both ICML 2019 and AISTATS 2019, where she pioneered initiatives to improve reproducibility in machine learning research. Her research group at UCSD focuses on trustworthy machine learning, with current projects spanning privacy-preserving AI, robustness against adversarial attacks, and methods for ensuring reliable out-of-distribution generalization. The group collaborates closely with the FAIR team at Meta AI, where Dr. Chaudhuri serves as a Research Scientist.