Find Academic Supervisors

Timothy B RoweView profile

Professor

• Vertebrate Paleontology
• Evolutionary Biology
• Phylogenetic Systematics
• +6 more

Timothy B. Rowe is a Professor (retired) at the Jackson School of Geosciences, University of Texas at Austin, affiliated with the Department of Earth and Planetary Sciences. He holds the J. Nalle Gregory Regents Professor title and has led the Vertebrate Paleontology Laboratory since 1998. His research focuses on vertebrate skeletal evolution, phylogenetic systematics, and high-resolution X-ray computed tomography (CT) applications for fossil analysis. Collaborations span global institutions, particularly in scanning significant fossils and developing digital morphology databases like DigiMorph.org. Education: Ph.D., Paleontology, University of California, Berkeley (1986) M.S., Anatomy, University of Chicago (1981) B.A., Geology, Occidental College (1975) Research Interests: Dr. Rowe’s work integrates evolutionary biology with cutting-edge imaging technologies. Key areas include mammalian and dinosaurian skeletal evolution, developmental patterns, and CT-based 3D morphological analysis. His fieldwork focuses on Mesozoic terrestrial sediments in Texas and the Southwest, uncovering critical fossil records. He pioneered open-access digital repositories for morphological data, revolutionizing paleontological research accessibility. Awards: Scientific American ‘World’s 50 Best Websites’ (2004) for DigiMorph.org UT Innovative Use of Instructional Technology Gold Award (2003) National Science Foundation Presidential Young Investigator Award (1989–1994) Advising & Grants: Supervised over 20 students (Ph.D., M.S., undergrad) and postdocs. Secured NSF grants for CT technology development, digital libraries, and fossil studies. Notable grants include the ‘Digital Library of Vertebrate Morphology’ (2002–2007) and ‘Acquisition of a High-Resolution Micro-CT Scanner’ (2007–2008). Labs & Collaborations: Directs the Vertebrate Paleontology Laboratory and co-founded DigiMorph.org. Active in field paleontology and international collaborations, particularly in Africa, South America, and Asia. His team’s work bridges traditional paleontology with digital informatics, emphasizing open data sharing.

• Robotics
• Tactile Sensing
• Machine Learning
• +1 more

Dr. John Lloyd is a Visiting Research Fellow at the University of Bristol's School of Engineering Mathematics and Technology. His research focuses on tactile robotics, sim-to-real learning, and robotic manipulation. Publication analysis reveals consistent themes in tactile perception (2020-2024), including deep reinforcement learning for robotic control, tactile sensor development, and applications in manufacturing and human-robot collaboration. Recent work emphasizes sim-to-real transfer and bimanual manipulation (2022-2023), while earlier publications established foundational methods for pose-based tactile servoing and defect detection (2020-2021). His collaborations include Prof. Nathan Lepora and Dr. Ben Ward-Cherrier at Bristol.

Laura LopezView profile

Associate Professor

• Supernovae and Gamma Ray Bursts
• Interstellar Medium
• Star Formation in Galaxies
• +3 more

Laura Lopez is an Associate Professor in the Department of Astronomy at Ohio State University, where she has been a faculty member since 2015. Her research focuses on supernovae (SNe), gamma-ray bursts, and the interstellar medium (ISM), with an emphasis on bridging observational data and theoretical models. She employs multiwavelength observations (radio to gamma-ray) to study stellar birth and death processes, particularly how supernova remnants reveal explosion geometries and how stellar feedback impacts star-forming regions. Education: Ph.D. in Astronomy & Astrophysics (2011), University of California, Santa Cruz M.S. in Astronomy & Astrophysics (2007), University of California, Santa Cruz B.S. in Physics (2004), Massachusetts Institute of Technology Research Interests: Lopez investigates supernova remnants' morphological signatures to distinguish between core-collapse and Type Ia explosions, and maps pressures from stellar feedback (radiation, winds, shocks) in regions like 30 Doradus. Her work also addresses galactic winds in starburst galaxies (e.g., M82) using X-ray and infrared observations. She leads the PHANGS multiwavelength galaxy survey, emphasizing star formation and feedback across the electromagnetic spectrum. Awards & Honors: 2022 AAS High-Energy Astrophysics Division Early Career Prize 2016 Annie Jump Cannon Award Cottrell Scholar (2019), NASA Group Achievement Award (2019) Multiple fellowships (Hubble, Einstein, Kavli) Advocacy & Outreach: Lopez co-chairs the AAS Committee on the Status of Minorities in Astronomy and co-leads the OSU Astronomy Diversity Committee. She expanded the Physics Masters-to-PhD Bridge Program to astronomy and advocates for STEM diversity through initiatives like BlackinAstro. Key Projects: PHANGS (Panchromatic Hubble Andromeda Treasury Survey) Advanced X-ray Imaging Satellite (AXIS) mission concept JWST observations of starburst galaxies

Charles TelescoView profile

Professor

• Cosmic Magnetic Fields
• Astrobiology
• Infrared Instrumentation
• +3 more

Charles Telesco is a Professor in the Department of Astronomy at the University of Florida, where he established the Astronomical Instrumentation Program in 1995. He holds a PhD in Astronomy and Astrophysics from the University of Chicago (1977), with prior NASA affiliations at Ames and Marshall Space Flight Centers. His research focuses on cosmic magnetic fields and astrobiology, leveraging instruments like CanariCam (a GTC facility instrument developed by his team) and the IMPS polarimeter for exoplanet studies. Key scientific contributions include mid-IR polarimetry of magnetic fields in star-forming regions and the development of compact polarimeters for biosignature detection. His work is supported by the NSF and involves collaborations with SETI, NASA, and NIST. Telesco's technical expertise spans infrared instrumentation, interstellar dust dynamics, and high-resolution observational techniques. Recent publications emphasize ultra-late-time spectroscopy of Type Ia supernovae (e.g., SN 2021aefx) using JWST, alongside advancements in polarimetry for both astrophysical and astrobiological applications. Active projects include refining IMPS for extraterrestrial biosignature detection and mid-IR surveys of nearby stars with CanariCam.

Stephen BradshawView profile

Professor

• Solar Astrophysics
• Plasma Physics
• Solar Flares
• +4 more

Stephen Bradshaw is a Professor of Physics and Astronomy at Rice University and serves as Chair of the Undergraduate Program. He leads the Solar Physics Group, focusing on solar astrophysics and plasma physics. His research examines solar flares, coronal heating mechanisms, and the dynamics of the solar wind. Bradshaw holds a MS in Planetary & Space Physics from Aberystwyth University (2000) and a PhD in Solar Astrophysics from Cambridge University (2004). His work integrates advanced numerical modeling with observational data from instruments like the Marshall Grazing Incidence X-ray Spectrometer (MaGIXS). Key research areas include non-equilibrium ionization processes in flaring loops, MHD wave dynamics, and the role of nanoflares in coronal heating. Bradshaw collaborates on critical science plans for the Daniel K. Inouye Solar Telescope (DKIST), advancing solar observation capabilities. Bradshaw’s publications often explore the interplay between theoretical models and observational data, emphasizing flaring loop thermal evolution, plasma diagnostics, and coronal rain phenomena. His contributions bridge laboratory plasma experiments (e.g., ultracold neutral plasmas) with astrophysical applications, revealing universal plasma behaviors.

Srinivasa G. NarasimhanView profile

Professor

• Computer Vision
• Computer Graphics
• Computational Imaging
• +5 more

Srinivasa G. Narasimhan is the U.A. and Helen Whitaker Professor of Robotics at Carnegie Mellon University's Robotics Institute within the School of Computer Science. He directs the Illumination and Imaging Laboratory (ILIM) and leads the Computational Imaging group, focusing on the physics of computer vision and graphics. His research develops novel imaging technologies for applications in robotics, transportation, medical imaging, and environmental sensing. Research interests span computational imaging, light transport modeling, and active perception systems. Key areas include: Physics-based vision for atmospheric and material interactions Novel camera designs and programmable lighting systems Robust perception for autonomous vehicles and medical diagnostics Non-line-of-sight imaging and computational scatterography Publications demonstrate strong emphasis on 3D reconstruction, computational optics, and vision systems for intelligent transportation. Recent works leverage self-supervised learning for dynamic scene understanding and develop novel sensors for medical and automotive applications. Awards and honors include: Best Paper awards at CVPR (2019, 2022), IV (2021), and ICCP (2020) Marr Prize Honorable Mention (ICCV 2013) Multiple demo awards at CVPR/ICCP Current advising includes 5 PhD students and 1 master's student. Major grants include NSF EXPEDITIONS (Computational Photo-Scatterography), DARPA REVEAL, and industry support from Ford, GM, Adobe, and Zillow. Manages multiple labs developing technologies like adaptive headlights, thermal imaging systems, and MHz-rate light steering devices.

Markus DesernoView profile

Professor

• Biological Physics
• Theoretical Biophysics
• Computational Biophysics
• +5 more

Markus Deserno is a Professor of Physics at Carnegie Mellon University's Mellon College of Science, Department of Physics, where he serves as Director of Graduate Affairs. His research focuses on theoretical and computational biological physics, investigating mesoscopic phenomena in lipid membranes, proteins, and other biological systems through the lens of fundamental physical principles. Educational background: Ph.D. from Max-Planck-Institute for Polymer Research (2000) Habilitation in Computational Physics from Mainz University, Germany (2006) Deserno's work centers on how thermal fluctuations, cooperativity, self-assembly, and elasticity govern biological systems at scales between atomic resolution and whole cells. He employs continuum elastic theories, differential geometry, and coarse-grained simulations to develop simplified models that capture essential physics without atomic detail. His research group actively explores membrane mechanics, phase behavior, and curvature elasticity, with particular emphasis on asymmetric lipid bilayers and their biological implications. Analysis of his 15 most recent publications reveals a dominant focus on membrane asymmetry (appearing in 8/15 papers), Gaussian curvature mechanics (6/15), and computational methodology development (12/15). His work increasingly integrates DNA nanotechnology with membrane systems and addresses thermodynamic consequences of lipid composition. Honors include: Thomas E. Thompson Award from Biophysical Society (2021) Otto Hahn-Medal from Max Planck Society (2001) As Section Editor for The Biophysical Journal and active member of four professional societies (American Physical Society, Biophysical Society, American Chemical Society, German Physical Society), Deserno maintains significant scholarly engagement. His research group, featuring members Nick, Mert, Martina, Muhammed, Nishchay, and Amirali, receives funding from federal agencies supporting computational biophysics research. The Deserno group operates within Carnegie Mellon's high-performance computing environment, developing specialized simulation tools for mesoscale membrane phenomena while maintaining strong collaborations with experimental biophysics laboratories worldwide.

• Radio Astronomy
• Astrophysics
• Pulsar Studies
• +3 more

Sam McSweeney is a Researcher at Curtin University's School of Electrical Engineering, Computer and Mathematical Sciences (EECMS), part of the Faculty of Science and Engineering. His work focuses on pulsar astrophysics, subpulse drifting phenomena, and radio transient detection using instruments like the Murchison Widefield Array (MWA). Key research areas include magnetar-like radio transients, nulling pulsars, and multiwavelength studies of neutron stars. He has contributed to major surveys like the SMART pulsar survey and developed advanced data processing pipelines for transient detection. Publications since 2023 highlight discoveries of long-period radio transients, emission state-switching phenomena, and multi-instrument follow-up strategies. His work bridges observational astronomy with computational techniques, emphasizing high-time resolution observations and GPU-based imaging for FRB searches.

• Computer Vision
• Medical Imaging
• Deep Learning
• +3 more

Yu Huang is a Researcher in the Department of Mathematical and Statistical Sciences at Clemson University's College of Science. His work focuses on advancing computational methods in medical imaging, computer vision, and deep learning applications. His research includes developing multimodal models for ophthalmology, enhancing 3D graphics rendering, and improving biomedical image analysis through active learning frameworks. Notable projects involve generative models for retinal imaging and shadow removal techniques in computer vision. His research interests span computational ophthalmology, neural rendering, and AI-driven biomedical solutions. Recent efforts emphasize scalable diffusion models for high-resolution synthesis and robust medical image segmentation using test-time augmentation. Yu Huang's publications reflect a strong emphasis on interdisciplinary research at the intersection of computer science and healthcare. His work addresses challenges in both foundational AI methodologies and their practical applications in clinical settings.

John M. M. AndersonView profile

Professor

• Image Processing
• Signal Processing
• Medical Imaging
• +3 more

John M. M. Anderson is a Professor of Electrical Engineering and Dean of the Howard University College of Engineering and Architecture (CEA). He holds a Ph.D. from the University of Virginia, an M.S. from Georgia Institute of Technology, and a Sc.B. from Brown University. His research focuses on signal/image processing, particularly for medical imaging modalities (e.g., PET, X-ray CT) and ground-penetrating radar (GPR) systems for landmine/IED detection. He has developed iterative MAP estimation algorithms and high-resolution imaging techniques using SLIM/SPICE frameworks. Education: Ph.D. in Electrical Engineering, University of Virginia (2002) M.S. in Electrical Engineering, Georgia Institute of Technology (1987) Sc.B. in Electrical Engineering, Brown University (1985) Awards: NSF CAREER Award Professional Roles: Past health science administrator at NIH/NIBIB Associate editor for IEEE Signal Processing Letters Grants & Partnerships: Recipient of DOE HBCU Clean Energy Education Prize Lead in $1.3M Microchip Partnership at Howard University Dr. Anderson’s work bridges academia and industry, emphasizing applications in biomedical imaging and defense technologies. His patents and collaborative projects reflect a commitment to advancing engineering education and innovation in HBCUs.

Ian DrydenView profile

Professor

• Statistical Shape Analysis
• Object-Oriented Data Analysis
• Functional Data Analysis
• +3 more

Ian Dryden is a Professor of Statistics at the University of South Carolina, affiliated with the McCausland College of Arts and Sciences. He holds a Ph.D. from the University of Leeds (1989) and has held academic positions at Florida International University, University of Nottingham, University of Chicago, and multiple stints at USC. His research focuses on statistical methodology for highly-structured data, including shape analysis, functional data analysis, and object-oriented data analysis. He has authored over 150 publications and two influential books: Statistical Shape Analysis (2016) and Object-Oriented Data Analysis (2022). Dr. Dryden’s work spans applications in medical imaging, corpus linguistics, environmental science, and Bayesian spatial modeling. Recent projects include using remote sensing for peatland monitoring and Bayesian hierarchical models for urban deprivation analysis in Tanzania. He has secured over $20M in grants as PI/Co-I and is an elected Fellow of the Institute of Mathematical Statistics. His research outputs emphasize methodological innovation, with key contributions to shape trajectory analysis, manifold-valued data regression, and Riemannian manifold smoothing splines. Collaborative work includes interdisciplinary projects in genetics, pharmacology, and environmental science, reflecting his broad statistical expertise and impact across disciplines.

Sarah JacquetView profile

Assistant Professor

• Paleobiology
• Sedimentology
• Functional Morphology
• +3 more

Dr. Sarah Jacquet is an Assistant Professor in the Department of Geological Sciences at the University of Missouri's College of Arts and Science. Her research focuses on paleobiology, sedimentology, and functional morphology of fossil groups, employing micro-CT, petrographic, and geochemical techniques to study taphonomic biases across geological periods. She investigates Cambrian small shelly fossils, Silurian jellyfish, and Eocene coprolites to reconstruct paleoenvironments and evolutionary patterns. Her educational background includes a Ph.D. in Biological Sciences from Macquarie University (2013-2016), Honours in Palaeobiology (2012), and a B.S. in Palaeobiology (2009-2011) from the same institution. Her recent publications predominantly explore Cambrian small shelly faunas, taphonomic methodologies, and geochemical proxies in sedimentary systems. Research trends show strong emphasis on integrating high-resolution imaging with stratigraphic and geochemical analyses to resolve preservation biases in the fossil record.

J. Quincy BrownView profile

Associate Professor

• Biomedical Engineering
• Cancer Diagnostics
• Biomedical Optics
• +3 more

J. Quincy Brown is an Associate Professor in the Department of Biomedical Engineering at Tulane University's School of Science and Engineering. His laboratory focuses on developing translational optical spectroscopy and imaging methods for improving cancer management in clinical settings, particularly surgical tumor removal. Research emphasizes device development and clinical validation through physician collaborations. Education: Ph.D. Biomedical Engineering, Louisiana Tech University (2005) B.S. Biomedical Engineering, Louisiana Tech University (2001) Postdoctoral Fellow, Biomedical Engineering, Duke University (2009) Research spans quantitative spectroscopy, fluorescence histology, structured illumination microscopy, and real-time diagnostic platforms. Work integrates optical engineering with oncology to develop clinical tools for tumor margin assessment and rapid biopsy analysis through collaborations with surgical teams. Recent publications demonstrate focus on advanced microscopy platforms (2021-2025), including PathCAM for digital pathology, light sheet microscopy optimizations using deep learning, and clinical validation studies of structured illumination for breast/prostate cancer. Technical innovations prioritize clinical translation through closed-loop systems and workflow integration. Awards and Honors: NIH NRSA Postdoctoral Fellowship (2006) Duke Cancer Center Young Investigator Award (2007) 4× AEMB Teacher of the Year (2013-2019) OSA Biophotonics Congress Chair (2019) Y Combinator recognition for Instapath Inc (2019) Tulane University Research Achievement Award (2021) NIH Imaging Technology Development charter member (2021-27) School of Science and Engineering All-Around Award (2022) Leads courses in Biomedical Optics (BMEN 6170) and Biomedical Signals/Systems (BMEN 3730/6730). Collaborates with clinical partners to develop intraoperative imaging solutions and validate diagnostic platforms in surgical oncology workflows.

Majid AhmadiView profile

Professor

• Digital Signal Processing
• Machine Vision
• Pattern Recognition
• +4 more

Dr. Majid Ahmadi is a Distinguished Professor in the Department of Electrical and Computer Engineering (ECE) at the University of Windsor's Faculty of Engineering. He holds a BSc from Sharif University of Technology (1971) and a PhD from Imperial College London (1977). His research focuses on digital signal processing, machine vision, VLSI systems, and memristive circuits. He is a Chartered Engineer (UK) and a Fellow of both IET and IEEE. His work spans cutting-edge areas including low-power VLSI architectures, illumination-invariant face recognition, and 3D IC testing. Recent publications address energy-efficient cognitive radio networks and microstrip filter optimization. His professional accolades include prestigious engineering fellowships. He advises graduate research in advanced signal processing and integrated circuit design. His contributions to IEEE Transactions and IET journals reflect his leadership in interdisciplinary electronics research.

Craig GlennieView profile

Professor

• Kinematic Remote Sensing
• Lidar Processing
• 3D Change Detection
• +6 more

Craig Glennie is a Professor in the Civil and Environmental Engineering department at the Cullen College of Engineering , University of Houston . He serves as the Director of the National Center for Airborne Laser Mapping (NCALM) and Director of Engineering Defense Research Initiatives . His research focuses on kinematic remote sensing, lidar processing, 3D change detection, and open-source software development. Education: Ph.D. and B.Sc. in Geomatics Engineering from the University of Calgary (1999, 1996). Professional Experience: Faculty member at the University of Houston since 2010, with prior industry roles at Terrapoint USA Inc. and Aerotec LLC. Research Trends: His recent publications emphasize advancements in lidar technology, flood modeling, volcanic CO2 monitoring, and applications of machine learning to geospatial data. Notably, he has contributed to NASA's Surface Topography and Vegetation missions and developed methods for ICESat-2 data analysis. Labs & Teams: Leads NCALM, a national facility for airborne lidar data acquisition and processing, and collaborates on defense research initiatives.