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Bharat BaruahView profile

Professor

• Nanotechnology
• Materials Science
• Environmental Chemistry
• +6 more

Bharat Baruah is a Professor of Chemistry at Kennesaw State University within the College of Science and Mathematics and Department of Chemistry and Biochemistry . He serves as the Director of International Programs , fostering global collaborations in basic and translational sciences while promoting international student recruitment. Education : MS (1998, Dibrugarh University, India), Ph.D. (2003, Indian Association for the Cultivation of Science/Jadavpur University, India), Postdoctoral Research (2003–2008, Colorado State University, USA). Research Interests : Environmental Remediation : Designing nanostructured metal oxides and plasmonic composites for photocatalytic degradation of toxic dyes under UV/visible light. SERS Detection : Developing gold/silver nanoparticles for ultrasensitive toxin detection at sub-micromolar/nanomolar concentrations. Corrosion Prevention : Creating robust coatings for aluminum alloys used in automotive, aerospace, and consumer goods. Transparent Wood Composites : Engineering flexible, conductive wood for sensors and energy storage devices. Scientific Trends : His 15 most recent publications focus on metal nanoparticle synthesis , photocatalysis , SERS substrates , and environmental toxin remediation , with recurring themes in green chemistry and textile-integrated nanomaterials . Grants & Collaborations : Research supported by unspecified organizations. He teaches courses like CHEM 3105 (Inorganic Chemistry) , CHEM 1212 (General Chemistry II) , and CHEM 4490/7900 (Industrial Chemistry and Entrepreneurship in Material Science) .

• Robot Learning
• Reward Inference
• Imitation Learning
• +5 more

Daniel Brown is an Assistant Professor at the University of Utah's Robotics Center and School of Computing . Previously, he completed a postdoc at UC Berkeley (2020–2022) and earned his PhD in Computer Science at UT Austin in 2020 under Scott Niekum . He leads the Aligned, Robust and Interactive Autonomy (ARIA) Lab , focusing on human-robot interaction and AI safety. Research interests include: Robot learning under uncertainty Reward inference from human input Robust policy optimization Bayesian reward inference High-confidence performance bounds Assistive AI systems Awards : NIH Trailblazer Award (2024) RSS Pioneer (2021) Teaching : CS 5955/6955 Advanced Artificial Intelligence (Spring 2025) CS 6300 Artificial Intelligence (Spring 2024) CS 5960/6960 Human-AI Alignment (Fall 2023)

Sarah Eberle-BlickView profile

Senior Lecturer

• Numerical Analysis
• Inverse Problems
• Partial Differential Equations
• +7 more

Sarah Eberle-Blick is a Senior Lecturer at the Institute of Mathematics , Goethe University Frankfurt, Department of Computer Science and Mathematics. Her work bridges numerical methods, inverse problems, and wave propagation in elasticity. Research Focus : Numerical analysis of PDEs, monotonicity methods for inclusion detection, FEM-BEM coupling, multiscale seismic data processing. Key Contributions : DFG-funded projects on elastic wave reconstruction; development of stable integral formulations for acoustic and thermoelastic wave equations; implementation of 3D wave simulations. Article Trends : Recent papers emphasize inverse problems in linear elasticity, time-harmonic wave equations, and multiscale analysis using wavelets. Collaborative work spans geophysics, computational mechanics, and mathematical modeling. Projects : Includes monotonicity-based regularization, Lipschitz stability estimation, and FEM-BEM coupling with convolution quadrature. Contact: eberle@math.uni-frankfurt.de | Room 103, Institute of Mathematics, Frankfurt am Main, Germany.

• Mathematical Biology
• Mathematical Cell Biology
• Developmental Biology
• +7 more

Maria-Veronica Ciocanel is an Assistant Professor of Mathematics (Primary) and Biology (Joint) at Duke University's Trinity College of Arts & Sciences, where she has held appointments since 2020. Her research sits at the intersection of mathematical biology, cell biology, and computational modeling, with a particular focus on intracellular transport mechanisms and protein organization in cells. Her research interests include mathematical cell and developmental biology, emergent dynamics in intracellular transport, multiscale modeling of filamentous protein organization, parameter inference for models of biophysics experiments, applied dynamical systems, and applied topological data analysis. She develops mathematical and computational techniques to uncover the basic functions of intracellular proteins in development and injury, with applications to RNA transport in frog oocytes, radial glial cells, and neurons. Her work combines differential equations, stochastic modeling, and topological data analysis to address fundamental questions in cell biology. Her recent publications demonstrate a consistent focus on mathematical approaches to biological problems, particularly in cytoskeleton dynamics, intracellular transport, and symmetry analysis in developmental systems. She also maintains an active research program in social justice applications, particularly analyzing racial disparities in federal sentencing through mathematical and statistical approaches. Lee A. Segel Prize for the Best Paper in the Bulletin of Mathematical Biology (2025) for 'Parameter identifiability in PDE models of fluorescence recovery after photobleaching' Red Sock Award (SIAG/Dynamical Systems) for best poster presentation Ciocanel actively mentors undergraduate students through various research programs including DOmath, PRUV, and Muser. Two of her former students, Anna and Niny, have gone on to become Assistant Professors at University of New Mexico and San Francisco State University, respectively. She has secured significant research funding including grants from the National Science Foundation and Pennsylvania State University for projects on microtubule behavior and membrane-actin cortex adhesion. She is also involved in the RTG: Training Tomorrow's Workforce in Analysis and Applications program as a Co-Principal Investigator. Her laboratory focuses on mathematical modeling of intracellular transport processes, with particular emphasis on microtubule dynamics in neurons and mRNA transport mechanisms. She collaborates extensively with experimental biologists at institutions including Brown University, Penn State University, and Duke University to ensure her mathematical models remain grounded in biological reality.

• Machine Learning
• Control Systems
• Reinforcement Learning
• +3 more

Benjamin Recht is a Professor at the California Institute of Technology , affiliated with the Center for the Mathematics of Information . His work spans Machine Learning , Control Systems , Reinforcement Learning , and Optimization , with a focus on theoretical guarantees, adaptive algorithms, and real-world applications. His research includes: Control Systems : Certainty equivalence, adaptive control, LQR, and robustness in dynamic environments. Machine Learning : Generalization bounds, interpolation in classifiers, test set overuse, and ethical frameworks for systemic harm detection. Neural Rendering : K-Planes for explicit radiance fields in space-time-appearance modeling. Recent publications (2025-2018) highlight trends in automating adaptive control , ethical machine learning , distributed computing , and 3D reconstruction . No student lists, awards, or lab details are explicitly mentioned.

Nathalie TrichesView profile

Researcher

• Biogeochemistry
• Climate Change
• Ecosystem Ecology
• +3 more

Nathalie Triches is a doctoral researcher at the Max Planck Institute for Biogeochemistry , affiliated with the Biogeochemical Signals (BSI) Department and the Integrating surface-atmosphere Exchange Processes Across Scales - Modeling and Monitoring (IPAS) research group . She is a PhD student in the International Max Planck Research School for Global Biogeochemical Cycles (IMPRS-gBGC) , focusing on greenhouse gas flux measurements in Arctic ecosystems. 2019-2021: MSc in International Masters of Soils and Global Change (Gent, Aarhus, and BOKU Vienna) 2013-2017: BSc in Forest Sciences (Bern University of Applied Sciences) Her research explores spatial and temporal patterns of N2O, CH4, and CO2 fluxes in Arctic microhabitats, emphasizing disturbances from global warming. She uses dark and transparent chamber measurements , UAV-based monitoring , and eddy covariance flux towers (e.g., at Stordalen Mire, Sweden). Collaborations include the University of Eastern Finland , University of New Hampshire , and University of Helsinki . Her publications address N2O emissions in nutrient-poor ecosystems , UAV-flux coupling methodologies , and permafrost carbon-nitrogen interactions . She collaborates on projects like N-PERM (Finland) and EMERGE (USA) to analyze microbial drivers of Arctic greenhouse gas budgets. She works with the Biogeochemical Signals Team and ICOS eddy covariance flux tower in Sweden, combining field data with modeling to improve understanding of Arctic GHG fluxes.

• Neuroscience
• Machine Learning
• Control Theory
• +4 more

Dr. LIU Quanying is an Associate Professor in the Department of Biomedical Engineering at the Southern University of Science and Technology (SUSTech), where she has been a faculty member since September 2019. She serves as the Principal Investigator of the Neural Computing and Control Laboratory (NCC lab) and is a doctoral supervisor. Prior to joining SUSTech, she earned her PhD in Biomedical Engineering from ETH Zurich and conducted postdoctoral research at Caltech. Education: PhD in Biomedical Engineering, ETH Zurich (2013-2017) Master in Computer Science, Lanzhou University (2010-2013) Bachelor in Electrical Engineering, Lanzhou University (2006-2010) Research Interests: Dr. Liu’s research integrates neuroscience, machine learning, and control theory. Her work focuses on multi-modal neural signal processing (EEG, sEEG, fMRI, DTI), explainable AI for neuroscience, and optimization techniques for neuromodulation (tES, TMS). She has developed high-density EEG source localization algorithms and data-driven brain network modeling frameworks, aiming to enhance precision in neural stimulation and control. Scientific Awards: The New Brain 30 (2023) AAIC Travel Award (2019) Estes Stars Award (2018) 深圳市孔雀人才计划C类 Laboratory and Team: As the PI of the NCC lab, Dr. Liu leads a team focused on machine learning algorithms, neurocomputational modeling, and neurofeedback control. The lab actively recruits graduate students, postdocs, and visiting researchers, emphasizing interdisciplinary collaboration in neuroscience and AI.

Quan ZhangView profile

Assistant Professor

• Machine Learning
• Statistics
• Interpretable Learning
• +7 more

Quan Zhang is an Assistant Professor in the Department of Accounting and Information Systems at the Broad College of Business, Michigan State University. With a Ph.D. in Information, Risk and Operations Management from the University of Texas at Austin (2020), an M.S. in Biostatistics from the University of Minnesota (2015), and a B.S. in Biological Science and Economics from Peking University (2012), his academic journey bridges diverse disciplines. Education Ph.D., Information, Risk and Operations Management - University of Texas at Austin (2020) M.S., Biostatistics (Ph.D. study) - University of Minnesota at Twin Cities (2015) B.S., Biological Science and Economics - Peking University (2012) His research focuses on interpretable machine learning , particularly balancing model interpretability and capacity using Bayesian and variational inference methods. Applications span telemedicine , quantitative marketing , medical data analysis , and online finance , with recent work on watermarking for copyright protection and robust representation learning. Selected publications include his 2025 paper on Beyond Invisibility: Learning Robust Visible Watermarks , 2023 work on Weibull Racing Survival Analysis , and foundational 2018 contributions to Nonparametric Bayesian Lomax Delegate Racing . His methodological innovations in Bayesian multinomial regression and survival modeling have been published in Advances in Neural Information Processing Systems and the Journal of Machine Learning Research . He contributes to software development with Python and R implementations for Bayesian models, available in his GitHub repository. His teaching includes graduate-level ITM885 Machine Learning and Optimization , emphasizing practical applications in business analytics.

Anna ÖsterholmView profile

Researcher

• Organic Electrochemistry
• Electrochromism
• Polymer Electrochemistry
• +2 more

Anna Österholm is a Principal Research Scientist at the Georgia Institute of Technology, affiliated with the College of Sciences and Department of Chemistry. She leads research in charge storage, electrochromism, and polymer electrochemistry within the Reynolds Research Group. Ph.D. in Analytical Chemistry, Åbo Akademi University (2011) Her work focuses on conjugated polymers and molecules, organic electrochemistry, and polymer processing. She manages the Organic Materials Characterization Lab (OMCL) in MoSE 1241, advancing electrochromic devices, conducting polymers, and redox-active materials. Recent research trends include optimizing electrochromic polymers for mid-infrared applications, exploring non-covalent interactions to enhance charge transport, and developing autonomous synthesis methods for electronic polymers. Key subfields span electrochromism, polymer physics, charge storage, and organic electronics. 2024 College of Sciences Outstanding Senior Research Faculty Award 2024 Inclusive Leaders Academy Culture Champion Certificate 2023 Research Faculty Community Leadership Impact Award 2018 Docent, Åbo Akademi University Her leadership in the OMCL core facility underscores her role in material characterization and interdisciplinary collaboration. Awards highlight her contributions to research and inclusive leadership.

Moira LauerView profile

Assistant Professor

• Polymer Chemistry
• Sustainable Materials
• Biomass Composites
• +3 more

Dr. Moira Lauer is an Assistant Professor of Chemistry at the University of North Carolina at Pembroke. Her academic journey includes a post-doctoral appointment at the Air Force Research Laboratory (AFRL) and doctoral research at Clemson University. She maintains affiliations with both UNCP and AFRL through her current and past research roles. Education: Ph.D. in Chemistry, Clemson University (2022) B.S., James Madison University (2017) Her research specializes in sustainable polymer chemistry with dual foci: developing eco-friendly composites from sulfur and biomass derivatives, and engineering infrared-transparent liquid crystalline elastomers via inverse vulcanization. This work bridges materials science and environmental sustainability, with applications in advanced optical materials and green technology.

• Nuclear Physics
• Knockout Reactions
• Nuclear Structure
• +2 more

LE BLEIS, T. is a nuclear physics researcher affiliated with GSI Helmholtzzentrum für Schwerionenforschung in Darmstadt, Germany. Their work focuses on experimental nuclear physics, particularly studying exotic nuclei structures and reaction mechanisms using advanced accelerator facilities. Research Focus: Primary interests include knockout reactions (proton and neutron), nuclear structure of exotic systems like halo nuclei, fragmentation processes, and implications for nuclear astrophysics. Key methodologies involve inverse kinematics experiments with radioactive beams and precision measurements using large-acceptance detectors. Recent Publications: Research from 2018-2024 demonstrates consistent focus on: Structure of light exotic nuclei (e.g., 17Ne, 13Be) Reaction mechanisms in intermediate-energy collisions Isotopic cross-section measurements Nuclear astrophysics connections via r-process nucleosynthesis studies Collaborations: Actively participates in international collaborations through the NUSTAR experimental program and other multi-institutional research initiatives at GSI facilities.