Find Academic Supervisors

Gierad LaputView profile

Adjunct Professor

• Human-Computer Interaction
• Machine Learning
• Sensing Systems
• +7 more

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.

• Bayesian statistics
• Stochastic simulation
• Markov chain Monte Carlo
• +5 more

Professor Chris Holmes is a Professor of Biostatistics at the University of Oxford, where he moved from Imperial College London in February 2004. He is a Fellow at St Anne's College and works in the Department of Statistics. His research focuses on applications and statistical methods development in genomic sciences and genetic epidemiology, holding a prestigious Programme Leaders Grant in Statistical Genomics from the Medical Research Council. Prior to his position at Oxford, Professor Holmes completed his doctorate in Bayesian statistics at Imperial College London, investigating novel nonlinear pattern recognition methods. This was followed by a post-doctoral position and then a lectureship at Imperial. Before his academic career, he worked in industry for several years in scientific computing, developing techniques for real-time pattern recognition models in defense and SCADA systems. Professor Holmes has a broad interest in the theory, methods and applications of statistics and statistical modeling, with a particular foundation in Bayesian statistics which he views as providing a unified framework for stochastic modeling and information processing. His specific research interests include: Bayesian statistics and stochastic simulation Markov chain Monte Carlo methods Pattern recognition and nonlinear, nonparametric methods Spatial statistics Statistical genetics and genomics Genetic epidemiology His recent publications (2023-2025) demonstrate a strong focus on the intersection of biostatistics, artificial intelligence, and healthcare applications. His work spans multiple domains including AI-driven disease classification in neurology, genomic data analysis for health equity, machine learning tools for healthcare prediction, and addressing bias in medical AI systems. A notable trend across his research is the application of advanced statistical methods to solve pressing problems in genomics, epidemiology, and medical diagnostics, with an increasing emphasis on health equity and the ethical implications of AI in healthcare. Professor Holmes currently supervises PhD students Oscar Clivio, Sahra Ghalebikesabi, and Natalia Garcia Martin. His research is supported by multiple grants, including the MRC Programme Leaders Grant in Statistical Genomics which funds his work in statistical genomics. He is actively involved in three research groups at Oxford that reflect the breadth of his scholarly interests: Computational Statistics and Machine Learning Statistical Genetics and Epidemiology Statistical Theory and Methodology

Juan Pablo BelloView profile

Professor

• Machine Listening
• Music Information Retrieval
• Signal Processing
• +1 more

Juan Pablo Bello is Professor of Music Technology and Computer Science & Engineering at NYU. He directs the Music and Audio Research Lab (MARL) and the Center for Urban Science and Progress (CUSP). He holds a Ph.D. in Electronic Engineering from Queen Mary University of London (2003) and a B.Eng. from Universidad Simón Bolívar (1998). Research integrates signal processing, machine learning and acoustics to analyze music/audio. Current projects include urban sound mapping, music structure discovery, and audio event detection. Funded by NSF and industry partners, with 100+ publications in IEEE/ACM venues. Honors include an NSF CAREER Award and Fulbright scholarship. He teaches graduate courses in music informatics and fosters industry-academia partnerships in audio AI.

Simon GodsillView profile

Professor

• Statistical Signal Processing
• Digital Audio Restoration
• Bayesian Computational Methods
• +5 more

Professor Simon Godsill MA PhD FIET FIEEE is a University Professor of Statistical Signal Processing in the Department of Engineering at the University of Cambridge. He heads a research team specializing in statistical signal processing, digital audio restoration, and Bayesian inference. His work addresses the processing and analysis of digital speech, audio, tracking systems, and financial datasets, with a focus on probabilistic modeling and computational methods. Research interests include statistical signal processing , degraded signal restoration , and Bayesian computational methods . Recent publications emphasize Gaussian processes, variational inference, and multi-object tracking for applications in audio enhancement and financial data analysis. He co-founded the audio remastering company CEDAR Audio Ltd in 1988. Scientific awards: Fellow of the Institution of Engineering and Technology (FIET) Fellow of the Institute of Electrical and Electronics Engineers (FIEEE) Outside academia, he enjoys singing, cricket, piano/organ playing, and running. His team at Cambridge's Engineering department focuses on robust tracking algorithms and signal enhancement techniques.

David W. JacobsView profile

Professor

• Computer Vision
• Machine Learning
• Object Recognition
• +11 more

David W. Jacobs is a Professor in the Department of Computer Science at the University of Maryland, with a joint appointment at the University of Maryland Institute for Advanced Computer Studies (UMIACS). He also served as the interim Director of the University of Maryland Center for Machine Learning starting in 2018. University: University of Maryland School: College of Computer, Mathematical, and Natural Sciences Department: Department of Computer Science Academic Rank: Professor Education: He received his B.A. from Yale University, and M.S. and Ph.D. in Computer Science from MIT. Research Interests: His research primarily focuses on computer vision and machine learning, particularly visual object recognition, lighting variation modeling, 3D reconstruction, perceptual organization, motion understanding, and the integration of vision with graphics and human-computer interaction. A major applied contribution is the development of Leafsnap , an electronic field guide app for plant identification, which has been downloaded over 1.5 million times and used in biodiversity and educational contexts. Publication Trends: His recent scholarly output centers on deep learning, convolutional networks, residual architectures, generative models (especially GANs), and interpretability. His work often bridges theoretical insights with practical applications in vision and AI. Scientific Awards: Honorable Mention, Best Paper Award, CVPR 2000 Best Student Paper Award, UIST 2003 Best Paper Award, Eurographics 2016 2011 Edward O. Wilson Biodiversity Technology Pioneer Award for Leafsnap Teaching and Advising: He has taught advanced courses such as CMSC 422 (Introduction to Machine Learning) and CMSC 828L (Deep Learning). He mentors students through course projects and research, though specific advisees are not listed. He has collaborated with institutions like Columbia University and the Smithsonian on impactful interdisciplinary projects. Labs and Teams: He is affiliated with UMIACS and leads research efforts in vision and learning, contributing to the University of Maryland Center for Machine Learning. His team has developed several mobile applications including Leafsnap, Birdsnap, and Dogsnap, demonstrating a strong focus on real-world deployment of vision technology.

Anantha ChandrakasanView profile

Professor

• Energy-Efficient Circuits
• Integrated Systems
• AI Hardware Security
• +3 more

Anantha Chandrakasan is the Vannevar Bush Professor of Electrical Engineering and Computer Science at MIT, serving as Dean of the MIT School of Engineering and Chief Innovation and Strategy Officer. His research focuses on energy-efficient integrated circuits, medical devices, and AI hardware security. He leads the MIT Energy-Efficient Circuits and Systems Group, developing systems for biomedical applications, wireless communication, and quantum computing. He holds appointments at MIT's Microsystems Technology Laboratories and has contributed to collaborations like the MIT-Takeda Program in AI-driven healthcare and a partnership with GlobalFoundries for energy-efficient AI chips. His work spans implantable drug delivery systems, conformable ultrasound patches, and secure edge computing architectures. Chandrakasan's innovations include ultra-low-power circuits for IoT devices, cryptographic processors for post-quantum security, and AI accelerators for edge applications. He emphasizes interdisciplinary research bridging electrical engineering with biomedical and quantum fields, supported by leadership roles in MIT's strategic initiatives. His contributions to energy-efficient computing have led to advancements in wearable health monitors, batteryless sensors, and secure communication protocols for medical devices. Ongoing projects include THz integrated systems and AI-enhanced analog circuit design optimization.

Wenyao XuView profile

Professor

• Embedded Systems
• Cyber-Physical Systems
• Body Sensor Networks
• +3 more

Wenyao Xu is a Professor and Associate Department Chair in the Department of Computer Science and Engineering at the University at Buffalo, School of Engineering and Applied Sciences. His research focuses on embedded systems, body sensor networks, biomedical signal analytics, telemedicine, and human-computer interaction. He leads the Embedded Sensing and Computing (ESC) Lab, pioneering innovations in wearable health monitoring and non-invasive medical technologies. Dr. Xu holds a PhD in Electrical and Electronics Engineering from UCLA (2013), an MEng in Embedded Systems from Zhejiang University (2008), and a BEng in Electrical Engineering from Zhejiang University (2006). He has received multiple accolades, including the UB Exceptional Scholar—Sustained Achievement Award (2021) and SEAS Senior Career Researcher of the Year (2020). His research spans cutting-edge areas like mmWave-based sensing for health monitoring, mHealth applications for behavioral interventions (e.g., Sense2Quit app for smoking cessation), and wearable devices for gait analysis and vital sign detection. Recent work includes advancements in through-clothing wound monitoring, voice- and gesture-based authentication systems, and AI-enhanced medical imaging. Publications reflect his interdisciplinary impact, with over 150 peer-reviewed papers in top venues. His lab collaborates closely with clinical partners to translate technologies into practical healthcare solutions. Dr. Xu also emphasizes mentorship, having been honored with the President Emeritus and Mrs. Meyerson Award for teaching excellence (2020).

Thomas BlumensathView profile

Professor

• Signal Processing
• Image Processing
• X-ray Tomography
• +6 more

Professor Thomas Blumensath is a Professor of Signal and Image Processing at the University of Southampton and a Fellow at the Alan Turing Institute. He is the Academic Lead in Image Processing and Reconstruction at the University's μ-VIS X-ray Imaging Centre and Director of Research at the Institute of Sound and Vibration Research (ISVR). His research focuses on advanced algorithms for solving inverse problems in tomographic imaging, combining machine learning, optimization, and statistical methods. Key areas include X-ray tomography strategies, GPU-accelerated reconstruction, and multimodal imaging applications. Education: B.Sc. (Hons) Music Technology and Audio System Design, University of Derby (2002) PhD in Electronic Engineering (Bayesian Signal Processing), University of London (2006) Research Interests: Professor Blumensath's work spans theoretical and applied signal/image processing, with emphasis on tomographic imaging techniques. His current projects address efficient reconstruction methods, spectral X-ray CT, and applications in manufacturing and plant science. He collaborates with advanced imaging facilities like Diamond Light Source and ISIS neutron imaging beamline. Key Contributions: His research bridges computational methods (e.g., compressed sensing) with practical imaging challenges, including limited-angle tomography and stereo imaging strategies. He leads the National Research Facility for Lab X-ray CT and has developed the TIGRE reconstruction toolbox. Grants & Projects: Active funding includes EPSRC projects on tomographic sensitivity monitoring and CT-based manufacturing inspections. Completed projects cover constrained reconstruction, AM process verification, and industrial CT metrology. Awards: Alan Turing Institute Fellowship Teaching & Leadership: He teaches modules on machine learning, biomedical image processing, and robotics. Leads the BEng Control Engineering program at the Joint Education Institute with Harbin Engineering University. Labs/Teams: Active in the Signal Processing, Audio and Hearing research group (SPAH) and the Institute for Life Sciences. Oversees the μ-VIS X-ray Imaging Centre's research initiatives.

Ambuj VarshneyView profile

Assistant Professor

• Wireless Communication
• Internet of Things
• Embedded Systems
• +4 more

Ambuj Varshney is an Assistant Professor at the National University of Singapore (NUS) School of Computing , leading the WEISER research group . His work bridges electronics, wireless communication, computer science, and AI with a focus on creating ultra-low-power embedded systems for sustainable IoT deployments. University of California, Berkeley: Postdoctoral Scholar (2020-2022) Uppsala University: PhD in Sustainable Networked Systems NXP Semiconductors: Software Engineer (prior to PhD) Bachelors in Information & Communication Technology Research interests center on overcoming wireless systems' energy asymmetry through tunnel diode oscillators , LiFi-RF hybrid networks , and battery-free communication architectures . His group develops STICORS —sticker-like computers for industrial and medical monitoring. Recent publications demonstrate AudioCast 's FM-band utilization for 130m transmission, TunnelSense 's vital monitoring, and PixelGen 's diffusion model cameras. These works combine IoT sustainability, spectrum efficiency, and hardware innovation . 2024: Google Research Scholar Award 2023: MobiSys Best Demonstration 2021: Berkeley FORM+FUND Fellowship 2019: ABB's $300K Research Award As an educator, he teaches CS4222 Wireless Networking and CS5272 Embedded Software Design . Past students include Wenqing Yan (NUS/UCB PhD), Qiao Yukai , and Kunjun Li . His team collaborates with Prabal Dutta (UCB), Christian Rohner (Uppsala), and Prateek Saxena (NUS).

Peter F. DriessenView profile

Professor

• Communication
• Signal Processing
• Control Systems
• +6 more

Professor Peter F. Driessen is a faculty member in the Department of Electrical and Computer Engineering at the University of Victoria, with a cross-appointment in the School of Music. He holds a BSc and PhD from the University of Victoria and is a Professional Engineer (PEng). His research focuses on communication systems, signal processing, control, and interdisciplinary projects in computer music and wireless technologies. Key areas include audio/video signal processing, radio propagation, sound recording, and multimedia systems. He leads the University of Victoria Propagation Laboratory, which explores radio wave propagation and Amateur radio integration with engineering education. His work spans theoretical research and applied projects like ECOSat satellite systems, software-defined radio (SDR), and innovative musical instruments such as the Radio Drum. He supervises undergraduate and graduate projects in these domains through ELEC 499 courses. Notable contributions include the APEGBC Editorial Board Award for Best Paper (2002) and patents in wireless networking and signal processing. His teaching includes courses in signal analysis and electromagnetics, and he collaborates on interdisciplinary programs like the Music/Computer Science degree. Education: BSc in Electrical Engineering, University of Victoria PhD in Electrical Engineering, University of Victoria Research Interests: Audio and video signal processing for music and media Software-defined radio and Amateur radio technologies Satellite communication and ground station development Gesture-based interfaces and musical instrument design Error mitigation in streaming audio/video Optical and microwave-photonic systems Labs & Collaborations: Propagation Laboratory (radio wave research) UVic Experimental Radio Group (Amateur radio club) UVic Satellite Design Team (ECOSat projects) UVic Centre for Aerospace Research Grants & Awards: APEGBC Editorial Board Award (2002) Multiple US patents in wireless systems and signal processing

• Computer Vision
• Machine Learning
• Deep Learning
• +7 more

Habib Ullah is an Associate Professor in Data Science at the Norwegian University of Life Sciences (NMBU), Norway, where he conducts research at the intersection of computer vision and machine learning. He is affiliated with the Institute of Data Science under the Faculty of Science and Technology. He has previously held academic positions at COMSATS University Islamabad, Pakistan, and the University of Ha'il, Saudi Arabia, and served as a postdoctoral researcher at The Arctic University of Norway. Educational Background: PhD in Information and Communication Technology (Computer Vision), University of Trento, Italy (2011–2015) MSc in Electronics and Computer Engineering, Hanyang University, South Korea (2007–2009) BSc in Computer Systems Engineering, NWFP University of Engineering and Technology, Pakistan (2002–2006) Habib Ullah's research is primarily focused on computer vision and machine learning, with applications in aquaculture, agriculture, and human behavior analysis. He investigates underwater fish feeding sounds using audio classification, develops zero-shot learning models for recognizing unseen classes, and applies deep learning to detect stress in salmon via skin dot patterns. He also explores AI-driven controlled environment agriculture, leveraging sensors and automation for optimal crop growth. His work emphasizes practical AI solutions for real-world challenges in environmental and biological domains. The recent publications highlight a strong trend in leveraging deep learning for zero-shot and semi-supervised learning, particularly in computer vision tasks such as sea ice classification, crowd anomaly detection, and agricultural monitoring. His research spans remote sensing, biomedical signal processing, and human activity recognition, demonstrating interdisciplinary versatility. The keywords reflect a focus on robust feature representation, knowledge transfer, and model generalization. Scientific Awards and Funding: Industrial PhD grant 'Advancing Controlled Environment Agriculture AI' from The Research Council of Norway (Project number 354125, 2 million NOK, 2024) Team member (Coordinator-Participant) in the Battery Cell Assembly Twin (BatCAT) project funded by Horizon Europe (7 mEuro, 2023–2027) Development of an AI-Based Image Analysis System for Monitoring Plant Status (Funding: 1.8 mNOK, starting 2025) Habib Ullah actively supervises PhD projects and contributes to academic service through editorial and organizational roles. He has served as an Associate Editor for IEEE Access, Guest Editor for MDPI Remote Sensing, and Editor of the Springer book Machine Learning Techniques and Sensor Applications for Human Emotion, Activity Recognition, and Support (ML-SHEARS) . He has also been a Track Chair and Program Committee Member for several international conferences, reflecting his leadership in the academic community. His research is supported by significant grants and collaborative projects, indicating strong institutional and international engagement. He is involved in multiple research teams and projects, including the BatCAT project on battery manufacturing and AI applications in controlled environment agriculture with RIFT LABS AS. His lab work integrates deep learning, sensor fusion, and data analytics for environmental and biological monitoring systems.

• Audio Signal Processing
• Spatial Audio Technology
• Medical Engineering
• +3 more

Craig Jin is an Associate Professor at the University of Sydney, leading the CARlab (Computing and Audio Research Laboratory) and Spatial Audio Research initiatives within the School of Electrical and Computer Engineering. He holds a BS from Stanford University, an MS from Caltech, and a PhD from the University of Sydney. His work focuses on immersive audio technologies, biomedical signal processing, and assistive technologies for sensory augmentation. Research interests include spatial audio reproduction, binaural processing, acoustic sensing for accessibility, and machine learning applications in signal processing. Key contributions span HRTF interpolation, noise reduction algorithms, and acoustic touch systems for the visually impaired. Recent projects include real-time MRI analysis of vocal tract dynamics and sparse recovery techniques for sound field reconstruction. His publications span over 150 peer-reviewed articles in journals like IEEE Transactions on Audio, Speech, and Language Processing, and conferences such as ICASSP. He advises four current PhD/Master’s students on projects like predictive gesture tracking, voice disorder classification, and magnetic resonance imaging techniques.

Reba WissnerView profile

Associate Professor

• Musicology
• Film Music
• Television Music
• +14 more

Dr. Reba Wissner is an Associate Professor of Musicology at the Schwob School of Music, Columbus State University, where she also serves as Interim Associate Dean in the Honors College. She specializes in film, television, and video game music, with a strong commitment to pedagogy and accessibility in higher education. Education: PhD, Musicology, Brandeis University, 2012 MFA, Musicology, Brandeis University, 2008 BA, Music and Italian, Hunter College, CUNY, 2005 Graduate Certificate, Higher Education Administration, Northeastern University, 2019 Graduate Certificate, Instructional Design, University of Wisconsin - Stout, 2020 Levels 1, 2, and 3 UDL Credentials, UDL-IRN Quality Matters Certified Peer Reviewer and Master Reviewer Dr. Wissner’s research centers on music in screen media, particularly Cold War-era television, and the intersection of music with cultural narratives. She is a leading voice in public musicology and Universal Design for Learning (UDL), advocating for inclusive and accessible music education. Her work spans historical analysis, pedagogical innovation, and interdisciplinary media studies. Her recent publications reflect a consistent focus on sonic storytelling, with books on The Twilight Zone , The Outer Limits , David Lynch, and Twin Peaks . She explores themes of nuclear anxiety, identity, and adaptation across media. Her editorial projects include the Oxford Handbook of Public Musicology and a guide on UDL in music history classrooms. Scientific Awards and Recognitions: Scholarship of Teaching and Learning Award, Columbus State University, 2022 Governor’s Teaching Fellow, Louise McBee Institute of Higher Education, 2022 Quality Matters Certified Peer Reviewer and Master Reviewer UDL Levels 1–3 Credentials Dr. Wissner has secured editorial contracts with Routledge and Oxford University Press, reflecting her leadership in the field. She mentors emerging scholars through collaborative editing and public scholarship initiatives. Her work in online learning and accessibility has shaped curriculum development and peer review standards. She leads the Public Musicology Certificate program and contributes to interdisciplinary studies and honors education. She is actively involved in research teams focused on media music and pedagogical innovation, integrating digital tools and inclusive design into music history education.

David CliftonView profile

Professor

• Clinical Machine Learning
• Digital Health
• AI in Healthcare
• +4 more

Professor David Clifton is the Royal Academy of Engineering Chair of Clinical Machine Learning at the University of Oxford’s Institute of Biomedical Engineering. He leads the Computational Health Informatics (CHI) Lab, focusing on AI-driven healthcare solutions with a strong emphasis on translational research in low- and middle-income countries (LMICs). His work spans digital health technologies, medical imaging analysis, and AI ethics. Clifton holds multiple fellowships, including from the Alan Turing Institute and Fudan University. Key affiliations include co-directorship of the Oxford-CityU Centre for Cardiovascular Engineering and involvement in the Wellcome Trust’s Flagship Centre in Vietnam. His research has been commercialized through spinouts like OBS Medical and Oxehealth. Notable projects include AI tools for non-invasive vital sign monitoring and pandemic response strategies using audio-based health data. Clifton’s awards include the IEEE Early Career Award (2022) and the Vice-Chancellor’s Innovation Prize. His lab’s Suzhou branch focuses on open-source digital health research using public datasets. Current research themes include multimodal data integration, generative AI in healthcare, and equitable AI deployment across global health systems.

Mark BockoView profile

Professor

• Audio and Acoustic Signal Processing
• Computer Audition
• Sensors and Transducers
• +3 more

Mark Bocko is a Distinguished Professor of Electrical and Computer Engineering at the University of Rochester, affiliated with the Hajim School of Engineering & Applied Sciences. He holds roles as Director of the Center for Emerging and Innovative Sciences (CEIS) and Director of Audio & Music Engineering. He earned his PhD in Physics from the University of Rochester in 1984, focusing on gravitational wave detectors. His research spans audio signal processing, sensors, superconductivity, and quantum computing. Notable contributions include flat-panel loudspeaker development, non-contact ECG sensors, and quantum coherence studies in Josephson junctions. Research interests include audio and acoustic signal processing, computer audition, and sensor technologies. His work integrates interdisciplinary approaches, combining electrical engineering, physics, and computer science. Awards include the 2012 Goergen Award for Teaching and Mercer Brugler Distinguished Teaching Professor (2008–2011). Recent publications address modal crossover networks for loudspeakers, vibrational touch sensing, and room impulse response modeling. He has advised PhD students on topics like spatial audio rendering and musical vibrato analysis. His labs focus on advancing audio engineering and smart sensor systems through collaborative industry partnerships.