Jonathan T. Barron is a Researcher at Google DeepMind in San Francisco, specializing in Computer Vision , Neural Rendering , and 3D Scene Reconstruction . He earned his PhD at UC Berkeley under Jitendra Malik and has pioneered advancements in NeRF (Neural Radiance Fields) and diffusion-based 3D generation. Research Interests : Computer Vision, Deep Learning, Generative AI, Image Processing, and 3D Reconstruction via Radiance Fields. His work includes Bolt3D for rapid 3D scene generation, CAT3D/CAT4D for text-to-3D/4D, and Zip-NeRF for anti-aliased radiance fields. He has also developed real-time rendering frameworks like SMERF and NeRF-Casting for reflections. Scientific awards: PAMI Young Researcher Award He has served as Area Chair for CVPR, ICCV, and NeurIPS, and his research is widely adopted in applications like Google's Lens Blur , Portrait Mode , and Jump VR .
Michael Baldea is an Associate Professor in the Department of Chemical Engineering at the University of Texas at Austin . He holds a Ph.D. in Chemical Engineering from the University of Minnesota (2006), with prior degrees from 'Babeş-Bolyai' University in Romania (M.Sc. 2001, Diploma 2000). His research group develops theoretical and computational methods for Process and Energy Systems Engineering , focusing on integrated decision-making, performance optimization, and process intensification with industrial validation. Education: Ph.D., Chemical Engineering, University of Minnesota (2006) M.Sc., Interface Process Engineering, 'Babeş-Bolyai' University (2001) Diploma, Chemical Engineering, 'Babeş-Bolyai' University (2000) Research Thrusts: Integrated decision-making in chemical/energy supply chains Process performance monitoring and optimization Process integration and intensification Key applications include grid-responsive chemical plants, intensified distillation/column designs, and renewable energy integration for building systems. Scientific Awards: Frank A. Liddell, Jr. Fellowship NSF CAREER Award (2015-2020) Moncrief Grand Challenges Faculty Award (2014) AIChE Outstanding Young Researcher Award (2017) Implementation : His group has translated research into commercial tools through partnerships with industrial test beds and is working to integrate methods into commercial simulators. They explore predictive approaches for building energy management and strategic capital investment analysis in next-generation energy systems.
Nima Mesgarani is an Associate Professor of Electrical Engineering at Columbia Engineering, Columbia University, affiliated with the Sense, Collect and Move Data Committee. His research bridges engineering and neuroscience through reverse-engineering neural signal processing mechanisms, leading to advancements in brain-machine interfaces, neural prosthetics, and speech processing algorithms. He received his PhD in Electrical Engineering from the University of Maryland and completed postdoctoral training at Johns Hopkins University's Center for Language and Speech Processing and UC San Francisco's Neurosurgery Department. Research Focus Professor Mesgarani's lab integrates computational neuroscience and engineering to study acoustic signal processing. Key areas include: Neural decoding of speech and auditory attention in multi-talker environments Development of brain-controlled hearing technologies Novel speech separation and synthesis algorithms inspired by cortical processing Cross-modal learning between auditory and visual systems Applications of large language models in neural signal interpretation Publication Trends Analysis of his 15 most recent articles (2025) reveals dominant themes: neural decoding techniques using intracranial EEG, brain-inspired speech separation models (e.g., Mamba architectures), applications of large language models in auditory neuroscience, cross-modal distillation methods, and clinical translation of audio processing algorithms. A strong emphasis emerges on real-time brain-computer interfaces and noise-robust speech processing. Laboratory and Collaborations Mesgarani directs an interdisciplinary lab developing neurotechnology for hearing restoration. His team collaborates with neurosurgery departments and speech processing centers, focusing on translating theoretical models into clinical brain-machine interfaces. The lab's work has yielded patents for brain-informed speech separation systems and attention-decoding frameworks.
Mohammad Peydayesh is a Lecturer and Senior Researcher at the Department of Health Sciences and Technology , ETH Zürich. He works in the Food and Soft Material Laboratory , focusing on sustainable materials derived from food waste and agri-food byproducts. PhD in Chemical Engineering (2018, Iran University of Science and Technology) Postdoctoral Fellow at ETH Zürich under Prof. Dr. Mezzenga Senior Assistant at ETH Zürich since 2021 His research spans soft matter , self-assembly phenomena , and amyloid fibril applications in: Environmental engineering (water purification, heavy metal removal) CO2 conversion and storage Smart packaging and bioplastics development Biorefinery concepts and circular economy Nanomaterials for waste valorization Recent publications highlight trends in amyloid-based hybrid materials for: Metal recovery from e-waste and contaminated water CO2 capture and conversion Bioplastic production from agricultural waste Antiviral and detoxification applications Water desalination and purification Photonic and catalytic materials He contributes to the Laboratory of Food & Soft Materials , advancing sustainable solutions through interdisciplinary material science.
Nadia Shardt is an Associate Professor in the Department of Chemical Engineering at the Norwegian University of Science and Technology (NTNU). Her research focuses on interfacial thermodynamics, particularly in systems with nanoscale curvature, with applications spanning atmospheric science, biomedical cryopreservation, and industrial process optimization. She contributes to teaching courses such as TKP4580 - Chemical Engineering Specialization Project and KP3100 - Chemical Engineering . PhD in Chemical Engineering (University of Alberta, 2019) BSc in Chemical Engineering (University of Alberta, 2015) Postdoctoral researcher at ETH Zurich (2020-2022) Her work addresses fundamental challenges in phase behavior under curvature constraints, combining microfluidic experimentation , Gibbsian thermodynamic modeling , and machine learning techniques to study systems like CO 2 storage media, cloud microphysics, and food emulsions. Recent publications emphasize surface tension modeling for complex multi-component systems and cryoprotectant loading efficiency. Scientific awards include the ETH Postdoctoral Fellowship Natural Sciences and Engineering Research Council of Canada (NSERC) Postdoctoral Fellowship Outstanding Academic Fellows Programme 2024-2028
Jesper Rindom Jensen is an Associate Professor in the Department of Electronic Systems at Aalborg University, Denmark, under the Technical Faculty of IT and Design. He is the Head of the Audio Analysis Lab, a leading research group in audio signal processing, since 2023. His work bridges theoretical signal processing and practical applications in artificial intelligence and audio systems. Full Name: Jesper Rindom Jensen Institution: Aalborg University School: The Technical Faculty of IT and Design Department: Department of Electronic Systems Research Lab: Audio Analysis Lab Email: jrj@es.aau.dk Office: Fredrik Bajers Vej 7B, B5-206, 9220 Aalborg Øst, Denmark Education: M.Sc. in Electronic Systems, Aalborg University (cum laude, 2009) Ph.D. in Signal Processing, Aalborg University (2012) Research Interests: Jesper Rindom Jensen's research centers on audio signal processing, with a strong emphasis on artificial intelligence, speech enhancement, noise reduction, beamforming, and multichannel systems. His work applies to diverse domains including robot and drone audition, spatial audio, and active noise control. He develops novel filtering techniques, including variable span linear filters and harmonic beamformers, to improve speech quality and intelligibility in noisy and reverberant environments. Publication Trends: His recent publications (2023–2025) show a strong trend toward integrating deep learning with classical signal processing, particularly in direction-of-arrival estimation, underwater acoustics, and robust multichannel systems. There is a clear focus on real-world applications, including sound zone control, active noise control, and limited-data scenarios using knowledge distillation. His work consistently emphasizes robustness, efficiency, and practical deployment. Scientific Awards and Recognition: AAU Talent for emerging research leaders Recipient of a competitive postdoc grant from the Danish Independent Research Council Advising and Grants: Jesper has supervised multiple PhD and master’s students, including Nørholm, Karimian-Azari, Zhang, and Wang. He has led significant research projects such as 'Sound Processing for Robots and Drones' (2018–2020) and participated in others related to joint audio-visual tracking and speech enhancement. His research has been supported by national funding bodies, reflecting its innovation and impact. Labs and Teams: He is a founding and core member of the Audio Analysis Lab at Aalborg University, which focuses on cutting-edge audio signal processing and AI-driven solutions. The lab fosters interdisciplinary collaboration and has produced numerous publications, datasets, and real-world applications. Jensen’s leadership since 2023 underscores his pivotal role in shaping the lab’s research direction.
David Warsinger is an Assistant Professor of Mechanical Engineering at Purdue University's College of Engineering, located in West Lafayette, IN. He holds a Ph.D. from MIT and degrees from Cornell University. His research focuses on thermofluids, nanotechnology, and membrane science applied to water treatment, energy systems, and sustainable technologies. Key areas include desalination innovation, the water-food-energy nexus, and HVAC efficiency. He leads the Warsinger Lab, which develops advanced membrane materials, dehumidification systems, and renewable energy integration for desalination. Education: Ph.D., Mechanical Engineering (MIT), M.Eng. and B.S. (Cornell University). Awards include the MIT Outstanding UROP Mentor (2015), UCOWR Dissertation Award (2016), and Purdue's Early Career Teaching Award (2025). His lab collaborates on grants from DOE, NSF, and industry, with recent focus on batch reverse osmosis, solar desalination, and membrane-based HVAC systems. Research emphasizes high-impact applications like atmospheric water harvesting, photocatalytic air purification, and CO₂ removal for space exploration. The lab's capabilities include membrane testing, multiphysics modeling, and partnerships with ARUP for sustainable building design. Current projects include NSF-funded ReNEW initiatives and innovations in wave-powered desalination. Awards include over $4M in grants, including a 2025 $75k Showalter Grant and a 2022 DOE $2.4M award. His lab publishes in top journals and presents at ASME, NAMS, and international conferences. Undergraduate and graduate training emphasizes diversity and entrepreneurship, with alumni securing roles at top universities and companies like SpaceX.
Prof. Xing Yang is a Professor at KU Leuven's Institute for Sustainable Metals and Minerals (ISM2), leading the Process Engineering for Sustainable Systems (ProcESS) research group. His work focuses on developing membrane-based technologies for sustainable resource recovery and environmental protection, with strong emphasis on metallurgical and wastewater applications. His research centers on advanced membrane engineering for separation processes, particularly membrane distillation, electrodialysis, and solvent extraction-based systems. Key interests include designing stimuli-responsive membranes, optimizing ion-selective transport, and developing energy-efficient processes for metal recovery from end-of-life batteries and industrial waste streams. His work bridges materials science, chemical engineering, and environmental sustainability to address critical resource scarcity challenges. Analysis of his 15 most recent publications (2024-2025) reveals a dominant focus on lithium and transition metal recovery through electro-driven membrane processes. He pioneers innovations in membrane architecture – including macrocycle-based channels, zwitterionic coatings, and PVDF modifications – to achieve unprecedented selectivity in complex matrices. His research consistently targets industrial applicability, with demonstrated applications in battery recycling, wastewater valorization, and CO 2 capture systems. The ProcESS research group operates within KU Leuven's Institute for Sustainable Metals and Minerals, collaborating across metallurgy, environmental engineering, and materials science disciplines. Their work integrates experimental membrane fabrication with process modeling to develop scalable solutions for circular economy implementation in resource-intensive industries.
Mohan Qin is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Wisconsin–Madison. Her research focuses on developing novel approaches for resource recovery from waste streams and the concentration and detection of microplastics in the Great Lakes. Dr. Qin received her educational degrees as follows: Ph.D. in Civil Engineering from Virginia Tech (2017) M.S. in Environmental Engineering from Peking University (2013) B.S. in Environmental Engineering from Shandong University (2010) Her primary research areas include: Bioelectrochemical systems for resource recovery from wastewater Environmental biotechnology for sustainable wastewater treatment Electrochemical processes for desalination and water treatment Membrane-based technology for selective ion removal Her work particularly emphasizes ammonia recovery from manure and wastewater, and the detection of microplastics in freshwater systems, contributing to sustainable water management and resource conservation. Analysis of Dr. Qin's recent publications (2022-2025) reveals a strong focus on ammonia recovery using membrane and electrochemical systems, with increasing attention to microplastics detection in lake water. Her work spans from fundamental transport mechanisms to practical applications in dairy manure treatment and Great Lakes monitoring, often integrating novel sensor technologies and renewable energy sources. Dr. Qin has received numerous awards, including: 2024 IWA Membrane Technology Specialist Group (MTSG) Rising Star Award 2024 University of Wisconsin-Madison Hilldale Undergraduate/Faculty Research Fellowship 2023 UW-Madison Media Fellow and Sustainability Fellow 2022 UW-Madison Madison Teaching and Learning Excellence (MTLE) Fellow Multiple awards during her graduate studies at Virginia Tech Dr. Qin actively mentors students through thesis and independent study courses (CIV ENGR 890, 990, 699) and has been awarded the Hilldale Fellowship for undergraduate research collaboration. Her research is supported by several fellowships including the UW-Madison Sustainability Fellow and Media Fellow, which likely fund her innovative work in resource recovery and microplastics detection. She leads a research group at UW-Madison focused on environmental biotechnology and electrochemical systems, collaborating with institutions like Yale University (where she completed her postdoc) and contributing to journals as an associate editor for Desalination and Water Treatment and on the early career editorial board of ACS ES&T Engineering.
Jia-Bin Huang is an Associate Professor in the Department of Computer Science at University of Maryland, College Park , with a secondary appointment at the University of Maryland Institute for Advanced Computer Studies . His work bridges computer vision , computer graphics , and machine learning . His research focuses on 3D scene reconstruction , neural radiance fields , generative models , and multimodal foundation models . He has made significant contributions to video super-resolution , text-driven 3D modeling , and inverse rendering techniques. 15 recent publications (2024-2025) at top venues: CVPR , NeurIPS , SIGGRAPH Asia , 3DV , and ECCV Pioneering work in Urban Scene Inverse Rendering , Generative Video Editing , and 3D Human Digitization He has received multiple awards including the 3M Non-Tenured Faculty Award , ETRA Best Paper , and NSF Grants . His lab trains 12 PhD students and has graduated 18 Masters/PhD students now at institutions like Stanford , Meta , and Google .
Martin Wolke serves as a Research Assistant at the Institute for Chemical and Thermal Process Engineering (ICTV) within the Faculty of Mechanical Engineering at Technische Universität Braunschweig. He works under the supervision of Prof. Dr.-Ing. Stephan Scholl and collaborates with senior researchers including Dr.-Ing. Wolfgang Augustin. His research interests span chemical engineering with specific focus on separation processes, distillation efficiency, and sustainable battery recycling technologies. His work examines how polymer additives affect vapor-liquid equilibrium and separation efficiency in distillation systems, particularly with non-volatile components and elevated feed viscosities. Wolke contributes to the BMWI-funded HVBatCycle project focused on HV battery recycling and resynthesis processes, specifically investigating electrolyte recycling pathways. His publication record demonstrates expertise in experimental measurement techniques for separation efficiency under challenging process conditions. As an educator, he supervises the Educational Lab 'Phase Equilibria' and teaches the 'Ionic Liquids' course, contributing to both practical and theoretical components of chemical engineering education at TU Braunschweig. His laboratory work leverages the university's specialized facilities for thermal process engineering, with potential applications in industrial separation processes and sustainable battery material cycles.
Dr. Qilin Li is a Professor of Civil and Environmental Engineering at Rice University, serving as Co-Director of the NSF Nanosystems Engineering Research Center for Nanotechnology-Enabled Water Treatment (NEWT). She leads research on advanced water treatment technologies, membrane processes, and nanotechnology applications to address global water challenges. Her work focuses on membrane distillation, nanomaterials for disinfection, and sustainable infrastructure solutions. Dr. Li holds a Ph.D. and M.S. from the University of Illinois at Urbana-Champaign and a B.E. from Tsinghua University. Her research group investigates membrane fouling, desalination, and recovery of valuable metals from wastewater. Key projects include nanophotonics-enhanced solar desalination and electrochemical processes for contaminant removal. She has been awarded the CAPEES/Nanova Frontier Research Award and the Roy E. Campbell Faculty Development Award. Dr. Li advises multiple Ph.D. students and postdoctoral researchers, advancing innovative water treatment technologies. Dr. Li’s contributions span academic leadership, industry collaborations (e.g., SolMem LLC), and policy-oriented research to enhance urban water resilience. Her lab develops materials and processes for energy-efficient water reuse, with a focus on sustainable solutions for industrial and municipal systems.
Dr. Mahdi Malmali is an Associate Professor in the Department of Chemical Engineering at Texas Tech University, affiliated with the National Wind Institute. He leads the Malmali Reaction and Separations Laboratory , focusing on advanced separation technologies and energy storage systems. Education Postdoctoral Research Fellow, Chemical Engineering and Materials Science, University of Minnesota (2015-2017) Ph.D. in Chemical Engineering, University of Arkansas (2014) M.S. in Chemical Engineering, Sharif University of Technology (2011) B.S. in Chemical Engineering, Razi University (2007) His research spans membrane-based separation , thermochemical energy storage , and process intensification , with applications in water recycling, renewable energy integration, and sustainable chemical production. Publications highlight innovations in laser-induced graphene membranes , vacuum membrane distillation , and low-pressure ammonia synthesis systems. Dr. Malmali has mentored PhD students including Bosong Lin and I-Min Hsieh, who have received accolades such as the 1st Prize in Student Poster Competition at the North American Membrane Society (2022). His lab's work addresses critical challenges in energy-efficient separation technologies and sustainable resource utilization.
Markus Schubert is Professor of Process Engineering at Dresden University of Technology's Faculty of Mechanical Science and Engineering, appointed in September 2022. Previously, he served as Group Leader for Fluid Process Engineering at Helmholtz-Zentrum Dresden-Rossendorf's Institute of Fluid Dynamics (2017-2022) and led the 'Mehrphasenreaktoren' group (2012-2016). His academic background includes: Doctorate (summa cum laude) in Mechanical Science and Engineering, Technische Universität Dresden (2007) Studies in Process Technology and Engineering, Technische Universität Dresden (1997-2003) Professor Schubert's research centers on multiphase flow phenomena and reactor innovation, with expertise spanning bubble column hydrodynamics, distillation tray efficiency, and advanced reactor systems including rotating and foam-based designs. His experimental and computational work addresses mass transfer optimization and flow pattern characterization in complex industrial processes. Analysis of his 2009-2023 publications reveals consistent focus on multiphase flow visualization and reactor design, particularly using X-ray tomography (ERC XFLOW project) and CFD modeling for distillation and bubble column systems. Key trends include the integration of advanced imaging techniques with process optimization for separation efficiency. His scientific recognition includes: ERC Grant for XFLOW project (Ultrafast X-ray tomography of turbulent bubble flows, 2013-2016) Professor Schubert has secured competitive research funding including the ERC grant and led international collaborations at institutions like Université Laval and UNSW. His work bridges fundamental hydrodynamics with industrial applications in chemical and process engineering. He currently leads process engineering research at TU Dresden, building on his leadership of the Fluid Process Engineering group at HZDR where he directed experimental facilities for multiphase flow characterization and reactor development.
Hannah O'Hern serves as a Clinical Assistant Professor in the Mechanical and Biomedical Engineering Department, specializing in fluid mechanics and energy systems with practical applications in renewable energy and water treatment. Her research spans fluid dynamics, renewable energy, and water resources engineering, focusing on Venturi nozzle design, hydraulic fracturing water management, and solar-thermal systems. She employs computational fluid dynamics and experimental methods to optimize engineering solutions for sustainability and performance. Recent publications (2020-2022) reveal consistent themes in fluid dynamics optimization and renewable energy integration, including Venturi nozzle performance studies, hydraulic fracturing water treatment reviews, and solar power plant retrofits. Her work emphasizes efficiency gains, environmental impact reduction, and practical engineering solutions across energy and water sectors.
