Vikas Singh is a Professor in the Department of Biostatistics at the University of Wisconsin-Madison, with appointments in Computer Sciences and Statistics. He also serves as a part-time Faculty Researcher at Google DeepMind. His research focuses on image analysis, machine learning, and medical imaging applications, particularly in neuroimaging and Alzheimer's disease studies. Singh holds a Ph.D. in Computer Science from SUNY Buffalo and has taught courses such as BMI/CS 767 (Medical Image Analysis) and CS 766 (Computer Vision). Affiliations: UW Computer Vision Group, Wisconsin Alzheimer's Disease Research Center (W-ADRC), Machine Learning@UW. Research: Develops algorithms for medical image analysis, including tools for neuroimaging and longitudinal biomarker studies. Grants: Collaborates on grants related to Alzheimer's progression modeling and imaging techniques. His work emphasizes interdisciplinary applications, bridging statistics, geometry, and optimization to solve real-world problems in healthcare and engineering.
Professor Bert Smith is a distinguished academic at the University of Oxford, serving as a Fellow of Lincoln College. He holds the position of Professor in the Faculty of Classics, with a specialization in Greek Archaeology and Roman Art/Archaeology. His academic journey includes MA, MPhil, and DPhil degrees from Oxford University. Prior to his current role, he was a Harkness Fellow at Princeton University (1983-85) and taught Hellenistic and Roman art at New York University’s Institute of Fine Arts (1986-1995). His research focuses on the art and visual cultures of the ancient Mediterranean, particularly the relationship between visual representation and social/political contexts. As director of the Aphrodisias excavation project since 1991, he has contributed significantly to understanding the archaeology of Greek cities in the Eastern Roman Empire. Key achievements include a British Academy/Philip Leverhulme Fellowship (2007-2008) and leadership in the AHRC-funded 'Last Statues of Antiquity' project (2009-2012), resulting in a collaborative book (2016). Teaching responsibilities include lectures and seminars on Greek and Roman art and archaeology. His publications span over four decades, with notable works on sarcophagi iconography, Aphrodisias excavations, and Hellenistic art. He actively collaborates with institutions like the Oxford Centre for Greek and Roman Antiquity (OCGRA). Current research continues to explore late antiquity art and archaeology through fieldwork and interdisciplinary projects.
Dr. Hillel Adesnik is a Professor in the Department of Neuroscience at the University of California, Berkeley, and a leading researcher in the neural basis of sensory perception. His lab focuses on cortical microcircuits, optogenetics, and neural coding, with emphasis on visual processing and memory formation. Key Research Areas: Cortical Microcircuits Optogenetic Tools Gamma Band Rhythms Neural Coding Mechanisms Dr. Adesnik has pioneered high-speed optical methods like 3D-MAP and 3D-SHOT to manipulate neural activity. His work spans cortical dynamics, synaptic plasticity, and cortical layer interactions, with applications in understanding learning algorithms and sensory inference. Selected Trends from Publications: Recent preprints and papers highlight advancements in cortical VIP neuron function, channelrhodopsin structures, and inter-areal computations. His team utilizes two-photon holography, cryo-EM, and computational modeling to decode perception-related neural codes. Scientific Awards: NIH Director's New Innovator Award (2013) Dr. Adesnik's lab collaborates with institutions like NIH and develops tools for awake animal studies. Funding includes grants from the Beckman Young Investigator Program and NIH.
Nicholas Antipa is an Assistant Professor at the University of California San Diego's Jacobs School of Engineering, in the Electrical and Computer Engineering department. His research focuses on the co-design of optical systems and algorithms to develop advanced computational imaging systems, leveraging innovations in 3D printing, sensors, machine learning, and AI. He holds a PhD in Computational Imaging from UC Berkeley and previously worked at the Lawrence Livermore National Lab on optical metrology for the National Ignition Facility. His work includes pioneering projects like the DiffuserCam and Miniscope3D, which enable high-dimensional optical signal capture and 3D microscopy. Education: PhD in Computational Imaging, UC Berkeley (2020) MS in Optics, University of Rochester Institute of Optics BS in Optical Science and Engineering, UC Davis Research Interests: Computational imaging systems, single-shot high-dimensional optical capture, lensless imaging, and applications in neuroscience and marine science. His lab explores novel optical designs, compressed sensing, and AI-driven imaging techniques to push the boundaries of conventional systems. Scientific Awards: Best Paper at ICCP 2019, 2016 Best Demo at ICCP 2017 No. 2 in Optica 15 Top-Cited Articles (2020) Affiliations: Director of the Computational Imaging Systems Lab at UCSD. Collaborates with institutions like Lawrence Livermore National Lab and the Scripps Institution of Oceanography for projects in marine sediment mapping and underwater object detection. His lab emphasizes open-source tools, such as the DiffuserCam Raspberry Pi tutorial.
Rong Pan is a Professor at the School of Computing and Augmented Intelligence, Arizona State University (ASU). He holds a Ph.D. in Industrial Engineering from Pennsylvania State University (2002), an M.S. from Florida A&M University (1999), and a B.S. in Materials Science from Shanghai Jiao Tong University (1995). His research focuses on quality and reliability engineering, design of experiments, time series analysis, and statistical learning theory. Key projects involve NSF-funded research on reliability prediction, accelerated life testing, and degradation modeling. He serves as an Associate Editor for the Journal of Quality Technology and has authored over 80 publications. Courses taught include Reliability Engineering, Design of Experiments, and Statistics for Data Analysts. His academic service includes roles as a referee for IEEE Transactions and IIE journals. Research interests emphasize statistical methods for reliability improvement, with recent work on Bayesian inference models, optimal experimental design, and machine learning applications in industrial systems. Grants include collaborations with the NSF, Arizona Department of Transportation, and Science Foundation Arizona. His work bridges theoretical advancements and practical applications in manufacturing, energy systems, and semiconductor reliability. Education: Ph.D. (2002), M.S. (1999), B.S. (1995) Key Research Areas: Reliability Engineering, Bayesian Methods, Time Series, DOE Active Grants: NSF CMMI, SUNY IT Visiting Scholar Program Teaching: IEE 573 Reliability Engineering, DSE 501 Statistics Service: Journal of Quality Technology (Associate Editor), IEEE Transactions (Referee)
Dr. Vadim Backman is the Sachs Family Professor of Biomedical Engineering and Medicine at Northwestern University's McCormick School of Engineering and Applied Sciences and Feinberg School of Medicine. He holds additional roles as Professor of Medicine (Hematology/Oncology) and Biochemistry and Molecular Genetics, Associate Director of Research Technology and Infrastructure at the Robert H. Lurie Comprehensive Cancer Center, and Director of the Center for Physical Genomics and Engineering. He earned his Ph.D. in Medical Engineering from Harvard-MIT and M.S./B.S. in Physics from St. Petersburg Polytechnic Institute. His research focuses on physical and biological science intersections, developing nanoscale imaging and computational technologies to study chromatin dynamics and their role in disease. Key areas include cancer diagnostics/therapeutics, chromatin engineering, and genome nanoimaging. Dr. Backman has published over 230 papers, holds 20+ patents, and leads large-scale projects like NCI Bioengineering Research Partnerships. Education: Ph.D. (Harvard-MIT), M.S. (MIT), M.S./B.S. (St. Petersburg Polytechnic Institute) Affiliations: PhD Programs in Applied Physics and Interdisciplinary Biological Sciences Research emphasizes chromatin's role in disease, with clinical translation for diagnostics and therapy. His lab develops technologies like nano-CHIA and ChromSTEM, advancing understanding of genomic organization and epigenetic regulation. Awards include the Cozzarelli Prize and MIT Technology Review's Top 100 Innovators. Awards: Cozzarelli Prize (2017), AIMBE Fellowship (2009), NSF CAREER Award (2003) Grants and collaborations include managing multi-investigator projects and co-founding biotech companies. Courses taught: BME 302 (Quantitative Systems Physiology), BME 429 (Advanced Physical and Applied Optics).
Prof. Margret Keuper is a Professor of Machine Learning at the University of Mannheim's School of Business Informatics and Mathematics, leading the Data and Web Science Group. She is also affiliated with the Max-Planck-Institute for Informatics and ELLIS (fellow since 2024). Her research focuses on robust deep learning, neural architecture search, and computer vision tasks like motion segmentation and adversarial defense. She holds a PhD from the University of Freiburg and previously held positions at the University of Siegen and the University of Mannheim. Her work spans projects funded by DFG and BMBF, including Climate Visions for social media analysis and TrackOpt for motion tracking. She teaches courses on computer vision, generative models, and reinforcement learning. She actively serves on program committees for top conferences like CVPR, ECCV, and NeurIPS, and is an associate editor for IEEE TPAMI and JAIR. Education: PhD in Computer Science from University of Freiburg (advisor: Thomas Brox) Research Projects: Learning to Sense (DFG), Climate Visions (BMBF), TrackOpt (BMBF) Key Roles: Head of Mannheim Master in Data Science Examination Board, Member of MSc Business Informatics Board Her research emphasizes robustness in AI systems, with contributions to adversarial attacks, domain generalization, and efficient solvers for large-scale problems. She advises over 15 PhD students across academic and industry partnerships.
Jianhua Xing is an Associate Professor in the Department of Physics & Astronomy at the University of Pittsburgh , affiliated with the Dietrich School of Arts and Sciences . His research focuses on applying physics-based approaches to study biological systems, particularly cell phenotypic transitions (CPTs) and their underlying dynamics. He integrates quantitative single-cell measurements with computational and theoretical analyses to understand how cells transition between stable states. Key research areas include: Nonequilibrium systems and rate theories for biological transitions Single-cell trajectory analysis and live-cell imaging Epithelial-mesenchymal transition (EMT) dynamics Gene regulatory networks and stochastic processes Biological applications of dynamical systems theory Recent work highlights the coupling between EMT and cell cycle arrest, leveraging machine learning frameworks (e.g., LivecellX ) for high-resolution imaging analysis. His lab also explores chromosomal dynamics and mechanotransduction in stem cell aging. Publications emphasize data-driven modeling and theoretical insights, with contributions to frameworks like GraphVelo and Graph-Dynamo for inferring cellular state transitions. Collaborative efforts bridge physics, biology, and computational science to address fundamental biological questions. No awards or grants are explicitly listed in the provided texts. His research group focuses on advancing systems biology through interdisciplinary methods, with a lab dedicated to quantitative analysis of cellular processes.
James M. Piret is a Professor at the University of British Columbia (UBC), affiliated with the School of Biomedical Engineering and the Michael Smith Laboratories. He holds a Sc.D. from MIT (1989), an S.M. from MIT (1986), and an A.B. from Harvard College (1981). His research focuses on bioprocessing, biomedical engineering, and cell therapy biotechnology, with emphasis on optimizing therapeutic cell production and biomanufacturing processes. Education : Sc.D. in Chemical Engineering, Massachusetts Institute of Technology (1989) S.M. in Chemical Engineering, Massachusetts Institute of Technology (1986) A.B. in Chemistry, Harvard College (1981) Professor Piret’s research integrates bioreactor engineering, Raman spectroscopy, and data analytics to advance cell-based therapies for diseases like cancer and diabetes. Collaborations with stem cell biologists (e.g., Drs. Kieffer and Levings) and engineers (Drs. Turner and Gopaluni) drive innovations in bioprocess optimization and device development. His lab emphasizes multidisciplinary approaches to accelerate biotechnology production processes and cell therapy manufacturing. Awards : William F. Meggers Award (2022) R.S. Jane Memorial Award (2015) Cell Culture Engineering Award (2012) Fellow, Chemical Institute of Canada (2004) His work includes developing novel methodologies for CHO cell glycosylation engineering, optimizing fed-batch bioreactor systems, and advancing Raman spectroscopy techniques for real-time cell analysis. The lab actively recruits motivated graduate and postdoctoral researchers to tackle high-impact challenges in biomedical and chemical engineering.
Georg Fantner is an Associate Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) with dual appointments in the School of Engineering (STI) within the Institute of Bioengineering and the School of Life Sciences (SV) for teaching. He directs the Laboratory for Bio- and Nano-Instrumentation (LBNI) and holds leadership roles including President of the Open Science Strategic Committee and the Association des Professeurs de l'EPFL. Research Focus: Bioinstrumentation, Nanotechnology, Scanning Probe Microscopy, and Metrology Teaching: Structural Mechanics for Life Sciences, Metrology, and Metrology Practicals His research pioneers advanced instrumentation for nanoscale characterization, emphasizing data-driven approaches to enhance microscopy techniques. Recent work integrates deep learning with scanning probe microscopy for real-time biological imaging and develops novel MEMS devices for fluid-compatible nanoscale manipulation. Key innovations include hermetically sealed sample chambers for pathogen studies and deterministic nanotopography engineering. Professor Fantner actively mentors 7 current PhD students and has supervised 14 graduates. His laboratory fosters interdisciplinary collaboration across engineering, physics, and life sciences to advance nanoscale measurement technologies and instrumentation development.
Cheuk Wai Tai is a Senior Staff Researcher at Stockholm University's Department of Environmental and Materials Chemistry since 2009. He manages the transmission electron microscopes and sample preparation equipment at the Electron Microscopy Center and serves as Section Editor for the Journal of Electronic Materials. His work focuses on quantitative structure characterization in functional materials research, particularly within nanoscience and nanotechnology contexts. Education: Ph.D. in Applied Physics, The Hong Kong Polytechnic University, 2004 M.Phil. in Applied Physics, The Hong Kong Polytechnic University, 2001 M.Sc. in Physics, The Chinese University of Hong Kong, 1998 B.Sc. (Hons) in Engineering Physics, The Hong Kong Polytechnic University, 1997 Dip. in Mechanical Engineering (Computer Aided Engineering), Institute of Vocational Education (formerly Haking Wong Technical Institute), Hong Kong, 1992 His research centers on structure-property relationships in functional materials through advanced electron microscopy techniques. Current specializations include Pair Distribution Function (ePDF) & Diffuse Scattering, Energy Materials characterization, and EM sample preparation methodology development. The group maintains strong focus on translating structural data into functional performance metrics for nanomaterials. Recent publications (2013-2019) demonstrate consistent emphasis on electron microscopy applications for energy storage materials (batteries, photocatalysts) and functional ceramics. Key trends include structural disorder analysis in piezoelectrics, development of quantitative TEM methods like SUePDF, and nanoscale characterization of electrocatalyst surface phases. His work bridges materials chemistry with advanced imaging techniques. Scientific recognition includes: Fellow of The Royal Microscopical Society (U.K.) Senior Member of IEEE Marie Curie Fellowship (2007-2009) from European Commission Sir Edward Youde Memorial Fellowship (2003/2004) from Hong Kong S.A.R. Government He teaches Solid State Chemistry (KZ7003) and leads Introduction to Analytical Electron Microscopy (KZ8009), having previously taught Advanced Transmission Electron Microscopy (KZ8010) before 2011. Major grants supporting his work include: "Quantitative structural characterisation using 3D electron-based pair distribution function" (Swedish Research Council) "A Multidimensional Toolkit for Modern Electron Microscopy" (Swedish Foundation for Strategic Research) "Mitigating Ni-rich Li-ion cathode side-reactions" (Swedish Energy Agency, Co-applicant) He leads the Cheuk-Wai Tai group within Stockholm University's chemistry department and oversees operations at the Electron Microscopy Center, where his team develops and applies advanced characterization techniques for functional materials research.
David A. Muller serves as the Samuel B. Eckert Professor of Engineering in the School of Applied and Engineering Physics at Cornell University and co-directs the Kavli Institute at Cornell for Nanoscale Science. His research group focuses on developing quantitative electron microscopy methods to understand materials properties at the atomic scale, with particular emphasis on sustainable energy applications and quantum materials. Muller's laboratory utilizes some of the world's highest resolution electron microscopes housed in specially designed, environmentally isolated rooms. Muller received his undergraduate education at the University of Sydney and earned his Ph.D. in Physics from Cornell University in 1996. Between 1997 and 2003, he was a member of the technical staff at Bell Laboratories, where he applied his expertise in imaging single atoms and atomic-scale spectroscopy to determine the physical limits of transistor miniaturization. In 2003, he returned to Cornell as a faculty member, where he has since established himself as a leader in advanced electron microscopy techniques. Muller's research spans multiple frontiers in materials science, with particular focus on understanding how electronic-structure changes at the atomic scale control macroscopic behavior in diverse systems like turbine blades, fuel cells, and transistors. His current work emphasizes the physics of renewable energy materials, atomic-scale control of materials to create electronic phases that cannot exist in bulk, and developing hardware and algorithms for 'big data' acquisition from high-bandwidth pixelated electron microscope detectors. His group's work bridges theoretical physics and experimental techniques, requiring researchers who can think in both real and reciprocal space while considering both fundamental principles and practical applications. Analysis of Muller's recent publications reveals a strong trend toward advancing electron ptychography and 4D-STEM techniques for atomic-scale imaging. His group has pioneered methods for 3D atomic-scale metrology, strain mapping, and imaging of radiation-sensitive materials. The research spans applications from semiconductor technology to quantum materials and energy storage systems, demonstrating the versatility of his microscopy approaches across multiple scientific domains. Top 100 Young Innovator by Tech Review Magazine (2003) Burton Medal from Microscopy Society of America (2006) Ernst Ruska Prize of German Society for Electron Microscopy (2021) John Cowley Medal from International Federation of Societies for Microscopy (2023) Fellow of American Physical Society Fellow of American Association for the Advancement of Science Fellow of Microscopy Society of America Muller has mentored an extensive group of students and postdocs who have gone on to successful careers in academia and industry. His former students hold faculty positions at institutions including Rice University, University of Southern California, Seoul National University, Colorado School of Mines, and the University of Michigan, among others. His research has been supported by substantial grants, including a $22.5M NSF grant that accelerates materials discovery. The Muller lab maintains close collaborations with the Kavli Institute at Cornell and PARADIM (Platform for the Accelerated Realization, Analysis, and Discovery of Interface Materials). The Muller lab operates at the forefront of electron microscopy, housing specialized instrumentation including high-resolution transmission electron microscopes in environmentally isolated rooms. The group collaborates extensively with other research teams at Cornell and worldwide, focusing on understanding materials atom by atom. Current research directions include applying machine learning to electron microscopy data analysis, developing cryogenic techniques for studying low-melting-point materials, and exploring quantum phenomena in engineered materials systems.
Dr. Qiang Lee is an Associate Professor in the Electrical and Computer Engineering Department at Hampton University, located in the Franklin W. Olin Engineering Building. She holds a Ph.D. in Electrical Engineering from Georgia Institute of Technology (2006), an M.S. in Computer Information Science from Clark Atlanta University (2002), and a B.Sc. in Electrical Engineering from Beijing University of Aeronautics and Astronautics (1995). Her research focuses on multi-modal sensor fusion, multiple target tracking, signal processing, and geospatial data analysis. Notable projects include NASA's ULI initiative on spectroscopy sensors for hypersonic flight control and ARL-funded work on sensor networks for target tracking. She has served as Principal Investigator (PI) on NSF and ARL grants, and co-investigator on NASA projects. Dr. Lee's publications span machine learning applications in spectroscopy, scramjet control systems, and multitarget tracking algorithms. Her work bridges aerospace engineering, data science, and sensor network optimization. She contributes to engineering education research, particularly in minority-serving institutions. Lab affiliations include Hampton University's School of Engineering research groups focused on sensor systems and aerospace applications. Grants highlight her role in advancing sensor technology for defense and aerospace industries.
Aaditya Rangan is an Associate Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He holds a Ph.D. from UC Berkeley (2003) and a B.A. from Dartmouth College (1999). His research focuses on applying numerical analysis and scientific computing to biological systems, including neuronal network dynamics in the insect olfactory system and mammalian visual cortex. He also develops computational tools for genomic data analysis, particularly biclustering methods for gene expression and SNP datasets. Rangan currently directs NYU's master's program in mathematics. Key contributions include models of synaptic depression in neural systems and algorithms for cryo-EM data processing. His work is published in journals like the Journal of Computational Neuroscience and PLoS Computational Biology , and his software tools are available on GitHub.
Johnny Powell is a Professor in the Department of Physics at Reed College, with a focus on astrophysics and computational physics. He leads research projects involving N-body simulations of galactic dynamics, supernovae explosions, and cosmological studies through the AGORA project. His work bridges theoretical astrophysics and experimental biophysics, with publications on DNA hydration, spectroscopy, and molecular biophysics. Primary affiliation: Reed College Physics Department Key research areas: Galactic dynamics, N-body simulations, DNA hydration, Raman/FTIR spectroscopy Professor Powell mentors thesis students in astrophysics, including work on barred galaxies, galactic bars, and cosmological simulations. His recent projects involve collaborations with institutions like the Max-Planck Institute for Solid State Research and Arizona State University's Lindsay Laboratory. Scientific grants include National Science Foundation (NSF) XSEDE awards for computational resources to study galactic bar simulations and cosmological zoom-in projects. His interdisciplinary research spans astrophysics and biophysics, with publications in journals like Biochemistry, Nucleic Acids Research, and Physical Review E. NSF Grant TG-AST150068: N-body simulations of galactic bars (50,000 SUs, 500GB) NSF Grant TG-AST150068: Buckling instability in galactic bars (25,000 SUs, 500GB) Outside academia, Powell is an avid birdwatcher with extensive field experience in Oregon, Brazil, Peru, and other global locations. He served on the Board of Directors of the Audubon Society of Portland (1998–2002) and has contributed to ornithological data via eBird. His educational collaborations include work with Stuart Lindsay at Arizona State University and Ludwig Genzel at the Max-Planck Institute.
