جستجوی استاد راهنمای دانشگاهی

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.

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

Chris Donahue is an Assistant Professor in the Computer Science Department at Carnegie Mellon University . He also serves as a part-time Research Scientist at Google DeepMind on the Magenta team. His work focuses on leveraging generative AI to enhance human creativity, particularly in music. Education: PhD in Computer Science (UC San Diego), Postdoctoral Scholar (Stanford University) His research spans controllable generative modeling of music and audio , with a focus on real-time interactive systems. Projects like Piano Genie , Beat Sage , and Copilot Arena demonstrate his commitment to real-world deployment. His Generative Creativity Lab (G-CLef) explores AI applications beyond music, including programming and natural language. Recent publications highlight advancements in multimodal music evaluation , real-time adaptation , and AI-driven sound morphing . He co-developed Magenta RealTime , an open-weight real-time music generation model, and MusicFX DJ Mode . Scientific Awards: Best Paper Award (top 1) at NAACL Student Research Workshop 2025 Best Paper Award (top 1% of submissions) at CHI 2025 Best Paper Runner-up at ISMIR 2021 He co-advises PhD students like Wayne Chi (NDSEG Fellow) and mentors Irmak Bukey . His lab receives support from the AIxArts incubator fund at CMU .

• Artificial Intelligence
• Machine Learning
• Music Technology
• +۵ مورد دیگر

Shinji Watanabe is an Associate Professor at Carnegie Mellon University's Language Technologies Institute and a Courtesy Professor in the Electrical and Computer Engineering department. He holds a Ph.D. (Dr. Eng.) from Waseda University, Japan, and has held research roles at NTT Communication Science Laboratories, Mitsubishi Electric Research Laboratories (MERL), and Johns Hopkins University. His research focuses on automatic speech recognition, speech enhancement, and machine learning for speech processing. Watanabe has published over 300 peer-reviewed papers and received the Best Paper Award at IEEE ASRU 2019. His work emphasizes robust speech processing in challenging environments, multilingual models, and neural audio codecs. He leads the ESPnet toolkit development for end-to-end speech processing systems and contributes to technical committees like IEEE SLTC and APSIPA SLA. Recent research trends include streaming speech systems, universal speech enhancement (URGENT challenges), and fusion of discrete speech units with self-supervised representations. He explores scalable speech foundation models through benchmarks like ML-SUPERB 2.0 and investigates cross-modal audio-visual processing in challenges like MISP 2025. Education : B.S., M.S., Ph.D. (Waseda University) Affiliations : CMU Language Technologies Institute, CMU ECE, Former roles at MERL and Johns Hopkins Key Projects : ESPnet, OpenWhisper-Style Models, URGENT Challenge Frameworks

• Automatic Speech Recognition
• Speech Enhancement
• Spoken Language Understanding
• +۳ مورد دیگر

Richard M. Stern is a Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU), holding courtesy appointments in the Language Technologies Institute and Department of Computer Science, and serving as an Artist Lecturer in the School of Music since 2007. His interdisciplinary work bridges engineering and music technology through the School of Music's programs. Education: Ph.D. in Electrical Engineering from Massachusetts Institute of Technology (MIT), 1976 Professor Stern's research spans sound, speech, hearing, and music, with core emphases on robust speech processing in variable acoustic environments, music information retrieval, automated accompaniment, and foundational contributions to binaural perception theory. His work integrates psychoacoustic principles with machine learning to address challenges in speech recognition and human-robot interaction. Recent publications (2022-2025) reveal intensified focus on deep learning for speech enhancement in reverberant/noisy conditions, human-robot interaction scenarios, and music tagging—highlighting innovations in beamforming, source separation, and temporal modulation modeling. Awards and Honors: Fellow of the IEEE Fellow of the Acoustical Society of America Fellow of the International Speech Communication Association (ISCA) ISCA Distinguished Lecturer Allen Newell Award for Research Excellence (1992) Lutron Award for Teaching Excellence (2018) Professor Stern has advised numerous graduate students in speech and audio research, though specific names are unlisted in source materials. His grant portfolio includes significant National Science Foundation and industry-funded projects in speech technology, with leadership roles in initiatives like Interspeech 2006. He actively collaborates with CMU's Language Technologies Institute and Music and Technology program. He maintains strong ties to CMU's interdisciplinary ecosystem through the Language Technologies Institute and School of Music's Music and Technology program, contributing to research that merges acoustic engineering with musical applications.

• Speech Recognition
• Audio Signal Processing
• Music Information Retrieval
• +۳ مورد دیگر

Fernando De la Torre is a Courtesy Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University, with an affiliation to the Robotics Institute where he has been a research faculty member since 2005. He holds a Ph.D. in Electronic Engineering from Ramon Llull University (2002). His research focuses on machine learning and computer vision, with applications in human health, augmented/virtual reality, generative models, and data-centric methodologies. He directs the Human Sensing Laboratory, which explores technologies for human behavior analysis and health monitoring. Notable contributions include founding FacioMetrics LLC (acquired by Meta), advancing facial recognition and 3D human digitization, and developing frameworks for robust visual models. His work bridges theory and practice, with over 225 peer-reviewed publications and editorial roles, including Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence. Recent research trends emphasize generative AI applications in satellite imagery analysis, VR/AR rendering optimizations (e.g., Gaussian splatting), and clinical motion recognition for healthcare. His projects often intersect with industry, addressing challenges in wearable health monitoring and immersive technologies. His lab collaborations span academia and industry, focusing on scalable solutions for 3D human modeling, adversarial robustness, and multimodal data fusion. Key achievements include pioneering work on 3D face animation from speech and garment reconstruction from single images.

• Machine Learning
• Computer Vision
• Augmented Reality
• +۴ مورد دیگر

Dr. Thomas Sullivan is a Teaching Professor in the Department of Electrical and Computer Engineering (ECE) at Carnegie Mellon University, with a courtesy Lecturer appointment in the School of Music. He holds a PhD (ECE '96) and BS (EE '85) from Carnegie Mellon and an MS in Computer Music from MIT's Media Lab (MAS '88). His research interests focus on signal processing for music and audio applications, including audio recording advancements, music synthesis, and controller design for synthesizers. He teaches core ECE courses such as Introduction to ECE, Senior Capstone Design, and Electro-acoustics, while also emphasizing STEM outreach for under-represented K-12 students. Education: PhD in Electrical and Computer Engineering, Carnegie Mellon University (1996) MS in Computer Music, MIT Media Lab (1988) BS in Electrical Engineering, Carnegie Mellon University (1985) His work bridges engineering and music technology, contributing to both academic and industry applications. Sullivan actively promotes STEM engagement through outreach programs and is involved in interdisciplinary initiatives like the Music Technology BS/MS programs. Outside academia, he enjoys music performance as an amateur guitarist/bassist, ice hockey, and distance running.

• Signal Processing
• Audio Engineering
• Music Technology
• +۴ مورد دیگر

Mayank Goel serves as an Assistant Professor in the Software and Societal Systems Department (S3D) at Carnegie Mellon University's School of Computer Science. His research bridges computer science and societal impact through practical sensing systems that leverage existing environmental devices for health monitoring and human-computer interaction without requiring hardware modifications. Dr. Goel specializes in mobile computing, signal processing, and machine learning to develop unobtrusive health technologies applicable to real-world scenarios. His core research areas include passive activity recognition for chronic disease management (particularly multiple sclerosis), privacy-preserving acoustic sensing, smartwatch-based clinical interventions for post-operative care, and equitable healthcare systems for global development contexts. He emphasizes end-to-end solutions through close collaboration with medical professionals and designers to ensure immediate deployability outside laboratory environments. Analysis of his 2024-2025 publications reveals a strong interdisciplinary focus spanning computer science, biomedical engineering, and clinical practice. Key trends include longitudinal digital phenotyping for neurological conditions, on-device privacy preservation in activity recognition, and multimodal procedural assistance systems. His work consistently addresses real-world challenges in sensor placement flexibility, user adoption barriers, and equitable access to medical technologies. No scientific awards were mentioned in the available documentation. Information regarding student advising, research grants, or laboratory affiliations was not specified in the provided materials, though his publication record indicates active collaboration with medical professionals and bio-engineers for clinical validation of health technologies.

• Mobile Computing
• Sensing
• Signal Processing
• +۷ مورد دیگر

Tom Sullivan is Teaching Professor in Electrical and Computer Engineering and Lecturer in Music at Carnegie Mellon University. His research interests center on signal processing applications for music and audio systems, including digital audio recording technologies, musical instrument synthesis, and controller interfaces for performance systems. Teaches foundational courses including Introduction to Electrical and Computer Engineering, Electro-acoustics, Signals and Systems, and core components of the Music Technology program. Actively involved in STEM outreach initiatives targeting K-12 students from underrepresented communities, demonstrating engineering applications in music and media contexts.

• Audio Signal Processing
• Music Technology
• Digital Audio Recording
• +۲ مورد دیگر

Laurie Heller is a Teaching Professor of Psychology at Carnegie Mellon University's Department of Psychology, affiliated with the Dietrich College of Humanities and Social Sciences. She holds additional affiliations with the Carnegie Mellon Neuroscience Institute (CMNI), CMU Music & Technology, CMU Center for Transformational Play, CMU CyLab Security and Privacy Institute, and the Center for the Neural Basis of Cognition (CNBC). Her research focuses on auditory perception, cognitive neuroscience, and the interplay between sound and human cognition. Education includes an S.B. in Brain & Cognitive Sciences from MIT, a Ph.D. in Psychology from the University of Pennsylvania, and a postdoctoral fellowship in Neuroscience at the University of Connecticut Health Center. Her work explores acoustic cues in sound event recognition, sound localization, and the effects of unwanted sounds. Collaborative projects include improving hearing aids, navigation aids for the visually impaired, and machine learning systems for sound classification. Recent research trends emphasize misophonia, environmental sound perception in hearing-impaired populations, and AI-driven sound synthesis. Her courses include Auditory Perception, Research Methods in Cognitive Psychology, and Music Cognition. She leads the Auditory Perception Lab and serves as an Associate Editor for the Journal of the Acoustical Society of America.

• Cognitive Neuroscience
• Cognitive Science
• Perception
• +۷ مورد دیگر

Alex Hills is a Professor of Engineering and Public Policy at Carnegie Mellon University (CMU), where he has held multiple leadership roles including Vice Provost, Chief Information Officer, and Founding Director of the Information Networking Institute. He led the development of 'Wireless Andrew,' the world's first large-scale Wi-Fi network, pioneering modern wireless campus infrastructure. His research interests include telecommunications, wireless networking, digital signal processing, and technology deployment in remote and developing regions. He focuses on error detection in wireless systems, policy implications of engineering solutions, and using technology for global development. The available article highlights his expertise in how digital networks handle voice data, particularly the causes of robotic distortion in phone calls due to packet corruption and error correction mechanisms. His work bridges engineering and public understanding of digital infrastructure. Distinguished Service Professor at CMU Author of books on Wi-Fi development and connecting remote Alaskan villages Senior advisor to 'Technology Consulting in the Global Community' program Expert quoted in Consumer Reports on telecom issues Alex Hills has advised numerous students through university programs and initiatives, particularly those focused on applying technology in developing nations. He has secured institutional support for global outreach projects and continues to mentor students in interdisciplinary tech-policy work. His career spans academia, government service (including as Alaska’s Deputy Commissioner of Administration), and military leadership. He leads or contributes to teams working on resilient communication networks, especially in underserved areas. His past and ongoing work involves building robust, low-infrastructure telecommunications systems suitable for remote communities and disaster response scenarios.

• Telecommunications
• Wireless Networking
• Digital Signal Processing
• +۷ مورد دیگر

Rita Singh is a Research Professor at the Language Technologies Institute within the School of Computer Science . Her research focuses on automated discovery, measurement, and learning of information from voice signals for voice intelligence. Contact information includes a phone number at 412-268-9859 . Developed generalizable speech processing algorithms (language-agnostic) and techniques for voice-based profiling of human parameters. Current work involves AI systems for genetic discovery, biomarker identification, and exploration of human physical state and psyche through voice. Research emphasizes scalable learning algorithms, voice signal analysis, and noise-resistant speech processing. She advises students such as Hira Dhamyal . Her methodologies integrate physics and bio-mechanics of voice production, enabling quantitative analysis of voice signals independent of semantic content.

• Speech Recognition
• Voice Signal Processing
• Artificial Intelligence
• +۴ مورد دیگر

Louis-Philippe Morency is an Associate Professor at Carnegie Mellon University's Language Technology Institute (LTI) within the School of Computer Science. He leads the Multimodal Communication and Machine Learning Laboratory (MultiComp Lab), focusing on computational foundations for analyzing human communicative behaviors during social interactions. His research integrates computer vision, machine learning, and social psychology, with applications in mental health and robotics. Education: Ph.D. in Computer Science from MIT's CSAIL. Previously held research roles at the University of Southern California's Computer Science Department. Research Interests include: Multimodal Machine Learning Artificial Social Intelligence Mental Health Computational Analytics Notable Awards: AI’s 10 to Watch (IEEE Intelligent Systems) NetExplo UNESCO Award 10+ Best Paper Awards at IEEE/ACM Conferences Advising 7 current Ph.D. students across interdisciplinary areas like multimodal learning and healthcare AI. Teaches advanced courses on multimodal machine learning and affective computing. His work has been featured in media outlets including The Economist, Wall Street Journal, and NPR. Labs/Teams: Director of MultiComp Lab, collaborating with interdisciplinary teams on multimodal systems and real-world social behavior analysis.

• Multimodal Machine Learning
• Artificial Social Intelligence
• Mental Health Computational Analytics
• +۳ مورد دیگر

Albert Gu is an Assistant Professor in the Machine Learning Department. His research focuses on advancing artificial intelligence and machine learning, particularly in recurrent neural networks, sequence modeling, and efficient computational architectures. He has contributed to state space models, distillation techniques, and applications in fields like bioinformatics and speech recognition. His work emphasizes balancing model efficiency with performance in handling long-range dependencies and complex data types. Key research interests include: Recurrent and state space models for sequence data Efficient neural network architectures Applications in genomics and speech processing Model distillation and knowledge transfer His recent publications (2023-2025) demonstrate a focus on model optimization, such as subquadratic architectures for biological sequences (Lyra) and bidirectional state space models for DNA analysis (Caduceus). He has also explored hybrid approaches combining transformers with recurrent components (HybriDNA) and advanced distillation methods for PDE modeling. No formal awards or grants are listed, but his work is notable for bridging theoretical advancements with practical applications in diverse domains.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Florian Metze is an Adjunct Professor at the Language Technologies Institute (LTI) within the School of Computer Science at Carnegie Mellon University. His work focuses on advanced speech and audio processing, multimodal learning, and machine learning applications in under-resourced languages. He leads research in speech recognition, generative audio models, and cross-modal understanding. His research interests emphasize leveraging audiovisual data for robust speech recognition, developing scalable solutions for low-resource languages, and advancing multimodal systems through innovations like diffusion-based text-to-audio generation (Audio-Journey) and modular neural architectures (Legonn). His contributions include foundational work on wake-word detection, speaker verification (MASV), and context-aware error correction in ASR systems. Collaborative projects span speech technology for unwritten languages, child phonetic acquisition modeling, and integrating visual context into speech processing pipelines. His work often bridges theoretical advancements with practical applications in edge computing, security, and biomedical signal analysis (e.g., heart sound monitoring). Metze's research outputs include over 100 peer-reviewed articles since 2018, with recent emphasis on large language models for multitalker scenarios, efficient neural architectures, and cross-modal representation learning. He actively contributes to open-source tools like the ACLEW DiViMe diarization toolkit.

• Speech Recognition
• Machine Learning
• Multimodal Learning
• +۳ مورد دیگر

Justin Chan is an Assistant Professor at Carnegie Mellon University, affiliated with the School of Computer Science (S3D Department) and the College of Engineering's Electrical and Computer Engineering Department. He leads the Semantic Signals Lab, focusing on creating mobile systems for physiological intelligence and augmented perception. His research spans AI-enabled healthcare systems, real-time mobile intelligence, and acoustic-tactile sensing for robotics and accessibility. Education: Ph.D. (expected 2023) in Computer Science & Engineering from the University of Washington, M.S. (2018) from the same institution, and a B.A. (2015) in Computer Science from Dartmouth College with high honors. Research Interests : His work combines wireless sensing, deep learning, and signal processing to develop technologies like smartphone-based ear infection detection (FDA-listed), newborn hearing screening (TUNE initiative in Kenya), and cardiac arrest detection via smart speakers. These innovations emphasize equitable healthcare access and real-world impact. Awards : SIGMOBILE Doctoral Dissertation Award Runner Up IEEE Pervasive Computing Emerging Rockstar American Academy of Audiology Research Award Grants & Projects : NIH-backed TUNE project, partnerships with NGOs like the Global Foundation for Children with Hearing Loss, and collaborations with startups like Wavely Diagnostics (FDA-listed ear infection tech) and Sound Life Sciences (acquired by Google). Leads development of privacy-preserving protocols for contact tracing (CovidSafe/WA Notify). Labs & Teams : Director of the Semantic Signals Lab, focusing on interdisciplinary projects merging hardware, software, and clinical applications.

• Digital Health Systems
• Mobile and Wearable Systems
• Wireless Sensing
• +۶ مورد دیگر