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

Kristen Grauman is a Full Professor in the Department of Computer Science at the University of Texas at Austin, where she leads the UT Computer Vision Group. Her research focuses on computer vision and machine learning, with applications in visual recognition, video analysis, and multi-modal perception. She received her B.A. from Boston College and her Ph.D. from MIT. Her research interests span visual recognition, image and video search, video analysis, first-person vision, embodied and multi-modal perception, and interactive machine learning. She has made significant contributions to the field, particularly in developing algorithms for understanding visual content and human activities from video, including foundational work on the Pyramid Match Kernel and relative attributes. Her recent publications reveal a strong emphasis on egocentric (first-person) vision, audio-visual learning, and view-invariant representations. There is a clear trajectory toward multi-modal integration (vision, audio, language) and real-world applications in instructional videos, human activity understanding, and embodied AI systems. She has received numerous awards including: AAAI Fellow (2019) J. K. Aggarwal Prize, International Association for Pattern Recognition (2018) Helmholtz Prize (2017) UT Austin Academy of Distinguished Teachers (2017) Best Paper Award, Asian Conference on Computer Vision (2016) Presidential Early Career Award for Scientists and Engineers (2014) Computers and Thought Award, International Joint Conferences on Artificial Intelligence (2013) Pattern Analysis and Machine Intelligence Young Researcher Award (2013) Alfred P. Sloan Research Fellow (2012) Marr Prize (2011) Prof. Grauman serves as Associate Editor-in-Chief for the IEEE Transactions on Pattern Analysis and Machine Intelligence. She has secured substantial research funding including the Presidential Early Career Award, NSF grants, and industry partnerships. Her advising has produced numerous influential publications and students who are now leaders in computer vision. She leads the UT Computer Vision Group, which collaborates closely with the Electrical and Computer Engineering Department. The group is pioneering large-scale egocentric video research through projects like Ego4D and Ego-Exo4D, focusing on real-world applications in human activity understanding, audio-visual perception, and interactive systems.

• Artificial Intelligence
• Computer Vision
• Graphics and Visualization
• +۸ مورد دیگر

Anna S. Mattila is the Marriott Professor of Lodging Management and serves as Professor-in-Charge of the Graduate Program at the School of Hospitality Management, Pennsylvania State University. Her research spans social media, service recovery, servicescape, prosocial behaviors, cross-cultural studies, and wellness in hospitality contexts. Education : B.S. and Ph.D. from Cornell University, M.B.A. from University of Hartford Mattila's work examines how emotions, cultural norms, and technological innovations like AI influence consumer decisions in hospitality. Her studies cover topics from robotic service interactions to sustainable food marketing and moral psychology in tourism. She has published extensively in journals such as International Journal of Hospitality Management and Journal of Consumer Marketing . Her recent articles focus on AI integration in hospitality, consumer trust in virtual influencers, upcycled food adoption, and the psychological effects of servicescape design. Key themes include ethical consumption, digital transformation, and cross-cultural consumer behavior. Advising : Supervised Anqi Luo's dissertation on service recovery in 2022 Mattila's office is located in 224 Mateer Building, University Park, PA. She contributes to advancing hospitality research through methodological consulting and the Penn State Biomarker Core Laboratory.

• Social Media
• Service Recovery
• Servicescape
• +۸ مورد دیگر

Dr. Jiamin Li is an Associate Professor at the School of Microelectronics, Southern University of Science and Technology (SUSTech), Shenzhen, China. She holds a Ph.D. and B.Eng. in Electrical and Computer Engineering from the National University of Singapore (NUS). Her research focuses on biomedical integrated circuits, energy harvesting, and wireless body-area networks. Education: Ph.D. and B.Eng. in Electrical and Computer Engineering from NUS Employment: Associate Professor (2024–Present), Assistant Professor (2022–2024) at SUSTech; Research Fellow at NUS Dr. Li's research addresses wireless body area powering , low-power biosignal interfaces , and intelligent biosignal processors . Her work leverages microsystem design for applications in biomedical engineering , particularly in energy-autonomous medical devices and human body-coupled communication . Her recent publications highlight advancements in concurrent power/data transmission , subharmonic pulse injection , and triboelectric energy harvesting for biomedical systems. Key venues include IEEE JSSC , Nature Electronics , and conferences like ISSCC and BioCAS. Awards: IEEE SSCS Predoctoral Achievement Award, ISSCC Best Demo Award, ASSCC SDC Best Design Award Committees: IEEE SSCS Women-in-Circuit (WiC) Committee, ISSCC Student Research Preview Committee Opportunities: Positions available for Ph.D., Master's, Postdoctoral Fellows, and Visiting Scholars

• Integrated Circuit Design
• Biomedical Microsystems
• Low-Power Sensor Interfaces
• +۳ مورد دیگر

David Lindlbauer is an Assistant Professor at the Human-Computer Interaction Institute (HCII) of Carnegie Mellon University, where he leads the Augmented Perception Lab and co-directs the CMU Extended Reality Technology Center. His research bridges human perception, extended reality (AR/VR), and computational interaction techniques, focusing on developing systems that dynamically adapt interface elements based on environmental context, user cognition, and task requirements. He completed his PhD at TU Berlin under Prof. Marc Alexa and held a postdoctoral position at ETH Zurich's Advanced Interactive Technologies lab. His work has been published extensively at top venues including ACM CHI, UIST, and IEEE VR, with research themes spanning gaze tracking, spatial audio optimization, haptic feedback, and multimodal notification systems. Media outlets like MIT Technology Review and Fast Company Design have featured his innovations. Dr. Lindlbauer has received prestigious grants from Meta, NSF, and ETH Zurich, and serves on program committees for CHI, UIST, and ISMAR. He has been recognized with Best Paper awards at ISS 2023 and CHI 2016, and his lab develops tools like MineXR for personalized XR interfaces and RealityReplay for temporal change visualization in mixed reality environments.

• Human-Computer Interaction
• Mixed Reality
• Augmented Reality
• +۸ مورد دیگر

Fei-Fei Li is the Sequoia Capital Professor in Computer Science at Stanford University and Founding Co-Director of the Stanford Institute for Human-Centered AI (HAI). She holds courtesy appointments in the Graduate School of Business and is a Senior Fellow at HAI. Her work bridges AI research with interdisciplinary applications in healthcare, robotics, and policy. Dr. Li pioneered the ImageNet dataset, instrumental in the AI revolution, and co-founded World Labs to advance spatial intelligence and generative AI. Education: B.A. in Physics, Princeton University (1999) Ph.D. in Electrical Engineering, Caltech (2005) Doctorate (Honorary), Harvey Mudd College (2022) Research Interests: AI ethics, computer vision, robotic learning, healthcare applications, and human-AI collaboration. Her teams developed frameworks like MOMA for activity recognition and BEHAVIOR for embodied AI benchmarks. She advocates for diversity in tech and co-founded AI4All to mentor underrepresented students. Key Contributions: ImageNet and ImageNet Challenge Stanford Vision and Learning Lab (SVL) Policy advisory roles for U.S. Senate, UN Secretary-General, and California Governor Labs & Initiatives: Leads the People, AI & Robots Group (PAIR), Partnership in AI-Assisted Care (PAC), and the Human-Centered AI Institute. Her work emphasizes ethical AI deployment and societal impact. Awards: VinFuture Prize (2024), IEEE Fellow, National Academy memberships (Engineering, Medicine, Arts & Sciences), and recognition as one of Time’s AI100 Influencers.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Aline Eid is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Michigan Ann Arbor, directing the Beam Dynamics research group. Her work bridges electromagnetics, wireless systems, and autonomous technologies, with a focus on mmWave/sub-THz sensing, backscatter communications, and wireless power transfer. PhD in Electrical and Computer Engineering (Georgia Tech, 2021) MS in Electrical and Computer Engineering (American University of Beirut, 2017) Postdoctoral Associate at MIT Media Lab (Signal Kinetics group) Research Interests: She pioneers systems that use electromagnetic waves to address societal challenges in Sustainable energy networks Smart cities/infrastructures Autonomous vehicles/robots Her group develops mmID tags for radar vision, batteryless sensors, and 5G-based wireless power grids. Scientific Impact: Recognized with awards including IEEE RFID Best Paper (2023) Proceedings of the IEEE Best Paper (2023) IEEE MTT-S Graduate Fellowship (2020) She leads commercialization efforts for warehouse robotics via MTRAC grant and startup Atheraxon. Advising: Mentors 8 students across PhD (Skanda Harisha, Sepideh Ghasemi, Yunfei Liu), Master's (Longyu Guo), and undergraduates (Mohamed Safawi, Katherine Shih, Andy Wang, Adrian Velazquez) in projects spanning robotics, IoT, and electromagnetic engineering. Labs: Beam Dynamics group at U-M Radiation Laboratory, collaborating with Atheraxon and MTRAC for technology translation.

• Applied Electromagnetics
• RF Circuits
• Wireless Sensing
• +۶ مورد دیگر

Andreas Rietbrock is Professor and Director of the Geophysical Institute (GPI) at the Karlsruhe Institute of Technology (KIT) , Germany, where he also serves as Dean of Studies for Geophysics . He is a leading expert in earthquake seismology, seismic tomography, and subduction zone dynamics, with a strong focus on integrating advanced observational techniques and computational methods. Education: While specific degrees are not listed in the provided text, his extensive publication record and leadership roles indicate advanced academic training in geophysics and seismology. Research Interests: His work spans a wide range of topics including: Seismic imaging of subduction zones (e.g., Nazca, Lesser Antilles) Earthquake rupture dynamics and fault mechanics Volcanic seismology and magma transport Full waveform inversion and AI-enhanced seismic analysis Distributed Acoustic Sensing (DAS) applications Induced seismicity and reservoir monitoring Research Trends: His recent publications (2022–2025) emphasize the use of dense seismic arrays, AI-based data processing, and multi-method tomography to study complex tectonic environments. Key themes include high-resolution imaging of slab structures, fluid migration in subduction zones, and the integration of DAS and machine learning for seismic monitoring. Scientific Contributions: Andreas has led major international projects such as the ANTICS Large-N deployment in Albania and the VoiLA project in the Lesser Antilles. He has published extensively in top-tier journals like Nature , Geophysical Research Letters , and Journal of Geophysical Research , with over 200 peer-reviewed articles. Teaching and Supervision: He teaches courses such as "Introduction to Geophysics II", "Seismology", and "Current Topics in Seismology and Risk". While specific student names are not listed, his role as Dean and principal investigator on numerous projects indicates active supervision of graduate students and postdocs. Labs and Teams: He leads the seismology group at GPI, coordinating large-scale deployments of seismic instruments, including ocean-bottom seismometers and fiber-optic DAS systems. His team collaborates globally with institutions in Europe, South America, and Asia.

• Earthquake Seismology
• Seismic Tomography
• Subduction Zone Processes
• +۴ مورد دیگر

Dr. Binbin Xie is an Assistant Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington (UTA). She holds a Ph.D. from the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst, where she was a recipient of the prestigious Google PhD Fellowship in Mobile Computing. Her research focuses on wireless sensing, mobile health, cyber-physical systems, smart IoT, and wireless networking. Dr. Xie’s academic journey includes a tenure-track position at UTA since September 2024. She has received notable awards such as the Emerging Rockstar (IEEE Pervasive Computing, 2025), Rising STARs Award (UT System, 2024), and multiple scholarships including the Krithi Ramamritham Computer Science Scholarship (2023). Her work spans innovative applications in wireless sensing, leveraging LoRa, mmWave radar, and RFID technologies for real-world challenges like in-vehicle sensing and indoor localization. Her recent publications highlight advancements in LoRa sensing coexistence with communication, mmWave radar human sensing using secondary reflections, and combating interference in multi-target localization. She actively contributes to the academic community through service roles, including Technical Program Committee memberships for MobiSys 2025 and EWSN 2025, and as a reviewer for top-tier conferences like IMWUT/UbiComp and journals such as ACM Transactions on Sensor Networks. Dr. Xie’s teaching includes courses like CSE 4321 (Software Testing & Maintenance). Her research and educational efforts aim to bridge theoretical innovations with practical implementations in wireless and IoT systems.

• Wireless Sensing
• Mobile Health
• Cyber-physical Systems
• +۵ مورد دیگر

Dr. Frank Rudzicz is an Associate Professor in the Faculty of Computer Science at Dalhousie University. His research lies at the intersection of artificial intelligence, natural language processing, and healthcare, with a focus on developing machine learning systems that improve clinical decision-making, patient outcomes, and accessibility in medicine. He holds a BSc from Concordia University (2004), an MEng from McGill University (2006), and a PhD from the University of Toronto (2011). His research interests include Natural Language Processing, Machine Learning, Healthcare, Speech Technologies, Explainable AI, and Fairness in ML. Dr. Rudzicz's recent publications span a wide range of topics, including Alzheimer's detection through speech analysis, surgical outcome prediction, mental health monitoring, privacy in AI, and the application of large language models in clinical settings. His work consistently emphasizes ethical AI, patient privacy, and real-world clinical integration. He has received several awards, including a Best Paper award at EMNLP 2020, a Best Student Paper award at ICASSP 2021, and the ISCA Best Student Paper award in 2013. His research has been published in top-tier journals such as Nature Scientific Reports , JAMA Network Open , IEEE Access , and Frontiers in Human Neuroscience , as well as leading conferences including NeurIPS, ACL, ICML, and Interspeech. Dr. Rudzicz supervises a dynamic research group working on AI for health, with active projects in voice-based diagnostics, ambient clinical documentation, explainable AI for surgery, and wearable-based monitoring for chronic diseases. He collaborates widely across disciplines, including with clinicians, neuroscientists, ethicists, and public health experts. He is also involved in major initiatives such as the Genetics Navigator study and Bridge2AI-Voice, aiming to build ethically sourced, diverse biomedical datasets. His lab actively explores the societal implications of AI in healthcare, including fairness, trust, and resistance to malicious fine-tuning.

• Natural Language Processing
• Machine Learning
• Healthcare
• +۱۲ مورد دیگر

Prof. Baker Mohammad serves as Professor and Director of the System on Chip Lab in the Department of Computer and Information Engineering at Khalifa University. With over 15 years of industrial experience at Intel and Qualcomm designing microprocessors and DSP chips, he bridges academic research with real-world engineering challenges in high-performance computing and low-power systems. His educational background includes: Ph.D. in Electrical and Computer Engineering, University of Texas at Austin (2008) M.S. in Electrical and Computer Engineering, Arizona State University B.S. in Electrical Engineering, University of New Mexico Dr. Mohammad's research spans cutting-edge domains where VLSI design converges with AI acceleration and emerging memory technologies . His work pioneers Memristor applications in environmental sensing (radiation, vacuum, glucose) and neuromorphic computing, while advancing energy harvesting systems for wearable electronics. The integration of in-memory computing with security primitives represents a paradigm shift in hardware design, moving beyond traditional CMOS limitations. His publication trajectory reveals accelerating focus on self-powered neuromorphic systems and RRAM-based architectures, with recent work (2021-2023) emphasizing hardware-software co-design for edge AI. Over 75% of his recent publications involve cross-disciplinary collaborations spanning materials science, chemistry, and biomedical engineering. Notable scientific recognition includes: IEEE TVLSI Best Paper Award 2016 IEEE MWSCAS Myrill B. Reed Best Paper Award Qualcomm Qstar Award for Performance Leadership KUSTAR IP Excellence Award Multiple SRC Techon Best Session Papers As a dedicated mentor, he has supervised over 15 graduate students while securing competitive funding from Khalifa University, ADEK, Qualcomm, Tii, and UAE space agencies. His grant portfolio demonstrates exceptional translational impact, converting fundamental research in memristive devices into drone flight computers and medical sensors. Current projects integrate academic rigor with industrial deployment timelines. The System on Chip Lab operates as a multidisciplinary hub where semiconductor physicists collaborate with AI researchers to develop RISC-V-based secure processors and piezoelectric nanogenerator systems. Recent expansions include partnerships with Tii for aerospace applications and medical device startups for glucose monitoring technology.

• Very-Large-Scale Integration (VLSI)
• Hardware Accelerators for Artificial Intelligence
• Hardware Security
• +۷ مورد دیگر

François Guimbretière is a Professor in the Department of Information Science at Cornell University, with affiliations to the Department of Design & Technology. He holds a PhD in Computer Science from Stanford University and previously served on the faculty at the University of Maryland before joining Cornell in 2009. His research centers on human-computer interaction, human-robot interaction, and interface design, with a strong emphasis on hardware/software co-design to create seamless, sustainable, and innovative interactive systems. His work spans multiple domains including interactive fabrication, remote collaboration, and low-power interfaces. Guimbretière's recent research explores novel fabrication methods such as interactive printing and yarn-based electronics, as well as immersive remote embodiment systems that reduce reliance on physical travel. These efforts reflect a broader trend toward sustainable and accessible interaction technologies. His publications demonstrate consistent contributions to top-tier venues in HCI and ubiquitous computing, focusing on tangible, wearable, and environmentally conscious interfaces. Scientific Recognition: He advises graduate and undergraduate researchers in the development of next-generation interactive systems. His lab supports projects in digital fabrication, remote presence, and sustainable design, often involving interdisciplinary collaboration. While specific grants are not listed, his research trajectory suggests sustained funding in human-centered technology innovation. He leads a research group focused on building practical, user-centered systems that bridge the digital and physical worlds through creative engineering and design. The lab emphasizes hands-on prototyping, real-world deployment, and the integration of traditional craft with advanced technologies.

• Human-Computer Interaction
• Human-Robot Interaction
• Interface Design
• +۷ مورد دیگر

Oswald Lanz is a tenured full professor at the Faculty of Engineering of the Free University of Bozen-Bolzano , leading the Visual Computing Lab . He holds a Ph.D. in Computer Science and a Mathematics degree from the University of Trento. Prior to his current role, he was a researcher and head of research at FBK Trento. He is an endowed professor collaborating with Covision Lab , an AI hub in Bressanone, and coordinates the board of professors for the PhD in Computer Science program since 2025. His research focuses on Computer Vision, Deep Learning, and Video Analytics , with applications in sports technology, medical imaging, and industrial automation. Key achievements include the Amazon AWS Machine Learning Research Award (2020) , ACM Multimedia Best Paper (2015) , and Best Student Paper at ICIAP (2007) . He co-organized the ELLIS-VISMAC Winter School (2025) and chaired ICIAP 2019 . His work spans novel view synthesis, action recognition, and anomaly detection, supported by patents in video tracking and detection. He teaches courses like Deep Learning and Artificial Intelligence in undergraduate and graduate programs. Recent projects such as 5VREAL integrate 5G, edge computing, and AI for sports analysis. His collaborations bridge academia and industry, exemplified by his role in Covision Lab and multidisciplinary initiatives like DSS4LCO for food supply chains. Lanz’s publications emphasize spatiotemporal modeling, neural architecture search, and hybrid machine vision systems.

• Computer Vision
• Deep Learning
• Machine Learning
• +۶ مورد دیگر

Anamitra Pal is an Associate Professor at Arizona State University's School of Electrical, Computer and Energy Engineering. His work focuses on power system resilience, renewable energy integration, and time-synchronized measurement analytics. He leads research initiatives combining artificial intelligence with grid modernization challenges. Ph.D. Electrical Engineering, Virginia Tech (2014) M.S. Electrical Engineering, Virginia Tech (2012) B.E. Electrical and Electronics Engineering, Birla Institute of Technology (2008) Prior to ASU, Pal conducted postdoctoral research at Virginia Tech's Network Dynamics and Simulation Science Laboratory (2014-2016). His research interests center on enhancing grid stability through: Advanced synchrophasor data analytics AI-driven power system security frameworks Renewable generation integration strategies Critical infrastructure protection during extreme events Recent publications demonstrate his focus on grid resilience during wildfires, deep learning-based state estimation, and innovative PMU applications. Pal has received multiple accolades including the NSF CAREER Award (2022) and IEEE Phoenix Section's Outstanding Young Professional Award (2019).

• Data Analytics
• Artificial Intelligence Applications in Power Systems
• Renewable Energy Integration
• +۳ مورد دیگر

Lee Miller is a Professor in the Department of Neurobiology, Physiology, and Behavior at the University of California, Davis, College of Biological Sciences. His research integrates neural engineering, physiology, and computational methods to develop communication restoration technologies and investigate sensory processing mechanisms. His primary research interests include neural engineering for speech neuroprosthetics, electrophysiological analysis of speech production, auditory neuroscience, and geometric approaches to neuromuscular signal decoding. He employs surface electromyography (EMG), electroencephalography (EEG), and computational modeling to study brain-machine interfaces for speech restoration and multisensory integration. Recent publications reveal a dominant focus on EMG-based speech neuroprostheses, with geometric and topological analysis of neuromuscular signals emerging as a key methodology. His lab has pioneered non-invasive approaches to speech articulation decoding, created standardized EMG databases, and investigated neural mechanisms of attention in speech-in-noise processing. This work bridges engineering innovation with fundamental neuroscience to address communication disorders. Professor Miller leads the Miller Lab at UC Davis, which specializes in neural engineering for communication restoration. The lab develops real-time speech synthesis systems from neural signals and investigates the physiological basis of speech production and perception using multimodal recording techniques.

• Neural Engineering
• Speech Processing
• Electrophysiology
• +۳ مورد دیگر