Ramana Vinjamuri is an Associate Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He holds a secondary appointment as Visiting Professor at the Indian Institute of Technology, Hyderabad, India. His academic journey includes a Ph.D. in Electrical Engineering from the University of Pittsburgh (2008), M.S. in Bioinstrumentation from Villanova University (2004), and B.Tech. in Electrical and Electronics Engineering from Kakatiya University (2002). Dr. Vinjamuri's research focuses on Brain-Machine Interfaces (BMIs) for upper-limb prostheses control , neuroprosthetics and exoskeletons , machine learning in motor control , and neurophysiological signal processing . His work extends synergy-based models to control 37-dimensional hand movements, addresses human-robot interaction through emotionally intelligent systems, and develops neurotechnologies for substance use disorder using wearable sensors and AI. NSF CAREER Award (2019) NSF IUCRC BRAIN Center Planning Grant (2020) Harvey N Davis Distinguished Teaching Assistant Professor Award (2018) His publications demonstrate expertise in EEG and EMG signal analysis , deep learning for motor decoding , synergy modeling , and humanoid robot control . The Vinjamuri Lab at UMBC involves graduate, undergraduate, and high school researchers, with international collaborations in India and the US.
Simon Masnou is a Full Professor at Université Claude Bernard Lyon 1, affiliated with the Institut Camille Jordan (CNRS UMR 5208). He holds leadership roles as Head of the 'Applied Mathematics, Statistics' Master's degree and Head of the 'M2 Maths in Action' program. Previously, he served as Director of the Camille Jordan Institute (2018-2022). His research focuses on applied mathematics, image processing, shape optimization, and geometric measure theory, with contributions to variational models, geometric flows, and applications in computer vision and materials science. Education: PhD in Mathematics (1998, Paris Dauphine) and HDR (2008, Paris 6). Research projects include ANR STOIQUES (2024-2028), PEPR PDE-AI (2023-2028), and collaborations with industry on topics like defect prediction in aluminum production and high-dimensional data analysis. Teaching includes courses on linear algebra, optimization, and machine learning at undergraduate and graduate levels. Key contributions span phase field models, varifold-based surface approximation, and image inpainting. He supervises PhD students in geometric variational problems and computational methods. His work bridges theoretical mathematics with industrial challenges, addressing issues in materials science, medical imaging, and cultural heritage preservation.
Diego Riveros-Iregui is a Professor in the Department of Geography at the University of North Carolina at Chapel Hill, recognized for pioneering research in ecohydrology and biogeochemistry. His work focuses on water-carbon-nitrogen interactions in tropical ecosystems, urban-rural gradients, and climate-impacted watersheds, employing high-frequency monitoring and advanced modeling techniques to address critical environmental challenges. His research interests center on tropical ecohydrology, particularly in Andean páramos and Galápagos Islands, examining carbon cycling in peatlands, nitrogen dynamics across land-use gradients, and stormwater impacts on urban streams. He integrates stable isotope analysis, machine learning, and field observations to study how geomorphology and climate variability regulate biogeochemical fluxes, with emphasis on vulnerable ecosystems facing anthropogenic pressures. Analysis of his 15 most recent publications (2021-2025) reveals dominant trends in high-resolution watershed monitoring, tropical carbon emissions, and urban hydrology. Key methodological innovations include machine learning for water use estimation, isotope-based tempestology, and bias correction in contaminant plume mapping, demonstrating interdisciplinary approaches spanning environmental engineering, climatology, and ecosystem science. Professor Riveros-Iregui has received the following scientific awards: Presidential Early Career Award for Scientists and Engineers (PECASE), the U.S. government's highest honor for early-career scientists, nominated by the National Science Foundation As a PECASE awardee, he leads NSF-funded research programs examining watershed processes across tropical and temperate regions. His collaborative work includes contributions to the EU-funded WELL CARE consortium on water security, though specific grant details and student mentorship records aren't documented in the source material. Current research priorities involve scaling point observations to watershed-level fluxes and assessing climate change impacts on island hydrology. While no dedicated laboratory is specified, his field studies leverage international partnerships in Ecuador's páramos and the Galápagos archipelago, focusing on microclimate-soil microbiome interactions and sustainable water resource management in high-elevation ecosystems.
Professor Rita Henderson is a Professor in the School of Chemical Engineering at UNSW and Deputy Dean (Societal Impact & Translation) at UNSW Engineering. Her research focuses on water quality and treatment, algal biotechnology, and sustainable engineering solutions. She leads the Algal and Organic Matter (AOM) Lab, collaborating closely with the Australian water industry to address challenges in algal blooms, membrane fouling, and cyanobacteria management. She serves as Editor for Water Research and AWWA Water Science , and chairs UNSW’s Sustainable Development Goal (SDG) Steering Committee. Education: PhD in Water Sciences (Cranfield University, 2008), MSc in Water Pollution Control Technology (2004), and MChem in Environmental Chemistry (University of Edinburgh, 2002). Her work integrates advanced analytical techniques, machine learning, and innovative separation methods to improve water treatment efficiency. Research interests include organic matter characterization, membrane technology optimization, and real-time monitoring of cyanobacterial blooms. Her contributions span algal harvesting via PosiDAF flotation systems, disinfection by-product formation, and sustainable wastewater pond technologies. She actively promotes equity, diversity, and inclusion in engineering education. Grants and collaborations highlight her industry partnerships, particularly in developing scalable solutions for water security. Her lab’s innovations aim to bridge gaps between research and practical applications, ensuring robust water treatment strategies for global challenges.
Christopher Bailey is a Professor of Advanced Semiconductor Packaging and Director of the Centre for Advanced Semiconductor Packaging at Arizona State University (ASU). He previously served as Professor of Computational Mechanics & Reliability and Associate Dean for Research at the University of Greenwich, UK. At ASU, he leads research on advanced semiconductor packaging, including roles as Principal Investigator (PI) and Co-Investigator (Co-I) on major projects such as the SRC-funded Thermo-Mechanical Modelling and US Chips Act initiatives (e.g., SWAP-Hub, SHIELD, ITSI). His research focuses on semiconductor packaging reliability, thermal management, co-design methodologies, and multiphysics modeling. Education: MBA (Technology Management), Open University, UK PhD, Thames Polytechnic, UK Research Interests: Advanced Semiconductor Packaging Thermal Management Solutions Co-Design and Multiphysics Modeling Reliability of Electronic Components His work integrates computational mechanics, materials science, and engineering to address challenges in high-reliability electronics. Recent projects emphasize predictive modeling for semiconductor packaging failures under thermal-mechanical stress. Awards: IEEE Region 8 Europe Award (2024) IEEE David Feldman Award (2022) Visiting Professorships at IIT Kharagpur (2018/2022) and Hong Kong (2018) Service & Leadership: Former President of IEEE Electronics Packaging Society (2020–2021) Associate Editor for IEEE Transactions on Components, Packaging, and Manufacturing Technology Conference Leadership (e.g., Program Chair for IEEE PAINE 2024) He has secured over $40M in research funding and authored 400+ archival papers, with expertise spanning industry collaborations (e.g., BAe Systems, Rolls Royce) and government advisory roles (EPSRC Peer Review College, UK Research Excellence Framework).
Tina Shoa is an Associate Professor in the School of Sustainable Energy Engineering at Simon Fraser University. She holds a Ph.D. in Electrical Engineering from the University of British Columbia (2010), an M.Sc. from the University of Manitoba (2004), and a B.Sc. from Iran University of Science and Technology (2000). Her research focuses on battery performance modeling, electrochemical methods for fault detection, sustainable battery manufacturing, and AI-based diagnostics. Education: Ph.D., Electrical Engineering, University of British Columbia, 2010 M.Sc., Electrical Engineering, University of Manitoba, 2004 B.Sc., Electrical Engineering, Iran University of Science and Technology, 2000 Research Interests: Battery performance modeling, analysis, and optimization Electrochemical and ultrasound-based battery fault detection Sustainable battery manufacturing processes AI-driven battery diagnostics Teaching and Courses: Advanced Battery and Fuel Cell Technologies Power Plant Systems Smart Grids Practicum SEE 354 D100 Energy Storage (Summer 2025) Patents: Battery State-of-health Determination upon charging (US Patent 11079437B2, 2022) Battery State-of-health Determination using multi-factor normalization (US Patent 10,302,709, 2019) Apparatus and Method for testing electrochemical systems (US Provisional Patent 62/994687, 2020) Key Contributions: Her work integrates electrochemical principles and AI to advance battery diagnostics and sustainable energy storage solutions. She has authored over 15 publications in top-tier journals and conferences, addressing battery aging, state estimation, and novel manufacturing techniques.
Prof. Sarthak Misra is a Full Professor in Medical Robotics at the University of Groningen’s Faculty of Medical Sciences, affiliated with the University Medical Center Groningen (UMCG). He leads research in the Robotics and image-guided minimally-invasive surgery (ROBOTICS) group and the Basic and Translational Research and Imaging Methodology Development in Groningen (BRIDGE) team. His work focuses on advancing medical robotics, microrobotics, and magnetic actuation technologies for surgical and biomedical applications. He holds an ORCID identifier and has published over 127 research outputs, including high-impact articles in journals like Advanced Materials Technologies and European Heart Journal . His research addresses challenges in minimally invasive surgery, soft robotics, and smart materials. Media engagements highlight his contributions to robotic surgery and addressing healthcare workforce shortages through automation. Research interests include: Microrobotics and fluidic systems Magnetic and acoustic actuation for medical devices Soft robotic systems for surgical applications Image-guided interventions 3D printing for biomedical robotics His recent articles emphasize innovations like MagNoFE3D printing and magnetic probes for endovascular interventions. Collaborations span global institutions, reflecting his interdisciplinary approach. No explicit awards are listed, but his extensive media coverage and 127+ publications underscore his impact. He is involved in grants, including OTP-funded projects, and mentors students in advanced robotics and medical engineering.
Noa Marom is an Associate Professor in the Department of Materials Science and Engineering at Carnegie Mellon University (CMU), holding courtesy appointments in Chemistry and Physics. She is a member of the Pittsburgh Quantum Institute (PQI) and an affiliate of the Wilton E. Scott Institute for Energy Innovation. Her research focuses on computational materials science, energy security, and quantum materials. Marom earned a B.A. in Physics and B.S. in Materials Engineering (cum laude) from the Technion-Israel Institute of Technology (2003) and a Ph.D. in Chemistry from the Weizmann Institute of Science (2010). She held postdoctoral positions at the University of Texas at Austin’s Institute for Computational Engineering and Sciences (ICES) before joining Tulane University as an Assistant Professor (2013–2016) and CMU in 2016. Her research interests include computational design of semiconductor materials, topological quantum computing, and crystal structure prediction. Key projects involve machine learning for materials discovery and quantum computing applications, such as optimizing semiconductor interfaces for stable qubits. Marom has received numerous awards, including the NSF CAREER Award (2016), DOE INCITE Awards (2017–2019), and the IUPAP Young Scientist Prize (2018). She serves as Associate Editor of npj Computational Materials. Her work spans collaborations with institutions like the Paul Scherrer Institute (Switzerland) and the Pittsburgh Supercomputing Center. Research highlights include computational studies of InAs/InSb semiconductors for quantum bits and machine learning-driven discovery of organic semiconductors.
Associate Professor Brett Jason Hallam is a Scientia Fellow at the University of New South Wales (UNSW), affiliated with the School of Photovoltaic and Renewable Energy Engineering. His research focuses on advancing solar panel manufacturing, particularly improving efficiency and reducing costs through innovations like selective emitter formation, laser processing, and hydrogen passivation. Key areas include understanding hydrogen passivation mechanisms to address defects in silicon solar cells and developing next-generation technologies such as TOPCon and SHJ cells. Education: PhD in Photovoltaic Engineering (UNSW, 2014), BE (First Class Honours and University Medal) and BSc in Physics/Oceanography (UNSW, 2009). He has held research placements at Fraunhofer Institute (Germany), Oxford University (UK), and IMEC (Belgium). Research Interests: Advanced hydrogenation for solar cells, material challenges for terawatt-scale PV deployment, reducing silver consumption, and sustainable manufacturing. Current projects include ARENA-funded initiatives on high-efficiency solar cell technologies and next-generation selective emitters. Notable Awards: 2018 Scientia Fellow, 2017 NSW Premier Prize for Energy Innovation, and 2016 Ulrich Goesele Young Scientist Award. His work addresses global energy transition goals, aiming for net-zero emissions by 2050. Industry Experience: Consultant for Suntech Power and Technology Transfer Team at UNSW, collaborating with companies like LONGi and Jinko. He advises on silicon solar cell technologies and has 16 patents. Labs/Teams: Active in UNSW’s PV research groups, focusing on scalable solar solutions and material science innovations.
Dr. Shalini Prasad is the Cecil H. and Ida Green Professor in Systems Biology Science and Associate Professor of Bioengineering at the University of Texas at Dallas (UT Dallas), affiliated with the Erik Jonsson School of Engineering and Computer Science. She also holds an adjunct professorship in Physics at Portland State University. Her research focuses on developing nanomaterial-based biosensors for medical diagnostics, environmental monitoring, and public health applications. She leads the Biomedical Microdevices and Nanotechnology Lab, which has supported over 50 researchers across graduate and undergraduate levels. Dr. Prasad earned her PhD in Electrical Engineering from the University of California, Riverside (2004) and B.E. in Electronics and Communication Engineering from the University of Madras (2000). Her career includes roles at Portland State University, Arizona State University, and Wichita State University. She has received prestigious awards, including the Bomhoff Distinguished Professorship and the Cecil Green Professorship, and holds over 30 peer-reviewed publications. Her research interests emphasize interdisciplinary approaches to create affordable, portable diagnostic tools for diseases like cancer, neurodegeneration, and cardiovascular disorders, as well as environmental sensors for soil health and water quality. Recent work includes wearable devices for monitoring inflammatory bowel disease and asthma, saliva-based THC detection, and low-cost CO₂/humidity sensors. Awards: Graduate Student Research Award (2004) Bomhoff Distinguished Professor of Bioengineering Cecil H. and Ida Green Professorship Advising & Grants: Dr. Prasad’s lab has secured funding from federal agencies and corporate partners, supporting projects like the SWEAT wearable for chronic inflammation tracking and soil health sensors. Her work bridges engineering, biology, and clinical applications, with a focus on translational technologies. Labs/Teams: Director of the Biomedical Microdevices and Nanotechnology Lab, collaborating on interdisciplinary projects with industry and academic partners.
Dr. Amit Goyal is the SUNY Empire Innovation Professor and SUNY Distinguished Professor in the Department of Chemical and Biological Engineering at the University at Buffalo. He serves as Director of the UB Initiative on Plastics Recycling and Innovation (a New York State Center of Excellence) and previously founded and led the RENEW Institute (2015–2021), focusing on interdisciplinary research in energy, environment, and water. His academic roles include leadership in both research and industry, with expertise spanning clean energy technologies, superconducting materials, and flexible electronics. Education: B. Tech in Metallurgical Engineering, Indian Institute of Technology (1996) MS in Mechanical & Aerospace Engineering, University of Rochester (1988) PhD in Materials Science & Engineering, University of Rochester (1991) Executive MBA, Purdue University International Executive MBA, Tilburg University Research Interests: Dr. Goyal’s work focuses on heteroepitaxial growth , strain-driven self-assembly , and advanced energy materials . He develops clean energy technologies, including superconducting devices and photovoltaic systems, while pioneering innovations in plastics recycling. His recent projects involve high-throughput plastic sorting techniques using molecular vibrational signatures and nanoscale defect optimization in superconducting wires. Advising & Industry: He co-founded TapeSolar Inc. and TexMat LLC, demonstrating expertise in venture capital engagement and technical project management. His RENEW Institute has attracted multidisciplinary collaborations across six schools at UB, advancing solutions for global sustainability challenges. Labs & Initiatives: Leads the NYS Center for Plastics Recycling & Innovation and oversees the UB Initiative on Plastics Recycling, integrating engineering, economics, and communication strategies to address plastic waste challenges.
Luis A. Ricardez-Sandoval is an Associate Professor in the Department of Chemical Engineering at the University of Waterloo and holds a Tier II Canada Research Chair in Multiscale Modelling and Process Systems. His research group develops advanced computational tools for optimizing chemical processes across multiple scales. Doctorate: Chemical Engineering, University of Waterloo (2008) MASc: Chemical Engineering, Instituto Tecnologico de Celaya (2000) BASc: Chemical Engineering, Instituto Tecnologico de Orizaba (1997) The research group focuses on multiscale modelling and process systems engineering , particularly for CO2 capture , energy systems , and heterogeneous catalysis . Their work combines advanced mathematics, machine learning , and uncertainty analysis to optimize chemical processes before physical implementation. Recent publications emphasize dynamic system optimization under uncertainty, multiscale simulation , and CO2 conversion technologies . Key methodologies include probabilistic uncertainty quantification and economic predictive control . Scientific Awards : 1997: First Place, XII National Creativity Contest 1998: Best Student Award, Instituto Tecnologico de Orizaba 1999: Third Place, XIV National Creativity Contest 2000: J.M. Smith Award for Best MASc Student He has collaborated with international institutions like CONACyT-Mexico, China Scholarship Council, and Universidad de Los Andes. His teaching includes graduate courses in process control, optimization, and computer-aided design.
Rainald Loehner is a Distinguished Professor of Fluid Dynamics at George Mason University's Center for Computational Fluid Dynamics. Since 2003, he has led the Center for Computational Fluid Dynamics at George Mason University. He is currently a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study (TUM-IAS) for 2023, hosted by Professors Kai-Uwe Bletzinger and Roland Wüchner in the 'Adjoint-Based System Identification of Large-Scale Structures' Focus Group. Loehner received his Diplom Ingenieur (Maschinenbau) degree from the Technical University of Braunschweig, and his PhD and a DSc in civil engineering from the University College of Swansea, Wales. After teaching at Swansea for a year, he worked at the Naval Research Laboratory in Washington, DC, followed by a research professorship at George Washington University. He joined George Mason University as an associate professor and was promoted to full professor in 1995 and distinguished professor in 2004. With over 35 years of experience, Professor Loehner's research spans the complete pipeline of numerical solvers and simulation tools. His expertise includes pre-processing, grid generation, numerical methods, field solvers, parallel computing, adaptive mesh refinement, fluid-structure interaction, shape optimization, system identification, and computational crowd dynamics. His current work focuses on developing advanced field solvers for compressible and incompressible flows, acoustics, electromagnetic wave propagation, heat and mass transfer, structural mechanics, and fluid-structure interaction. Key application areas include blast mitigation, ship hydrodynamics, blood flow, contaminant transport, and pedestrian safety. Loehner's recent research output (2020-2024) shows a strong trend toward digital twin technology and adjoint-based methods for structural analysis and optimization. His publications focus on high-fidelity digital twins for detecting structural weaknesses, risk assessment in engineering systems, and optimization of sensor placement. His work bridges computational mechanics with machine learning approaches, particularly in system identification and inverse problems, demonstrating how computational methods can solve complex real-world engineering challenges. 2020: Ranked #15119 in the Stanford List of Most Influential Scientists of the World; #8 in Aerospace and Aeronautics 2010: Distinguished International Career Award, Argentine Association of Computational Mechanics 2008: Fellow, International Association for Computational Mechanics 2006: Associate Fellow, AIAA 2005: Honorary Professor, University of Wales Swansea 2005: Advisory Professor, Shanghai Jiao Tong University 2004: Distinguished Professor of Fluid Dynamics, George Mason University 1999: Computational Mechanics Achievements Award, Japan Society of Mechanical Engineering 1993: Doctor of Science in Civil Engineering, University College of Swansea 1979-1983: Studienstiftung des Deutschen Volkes (Top 1% of German Students) Professor Loehner has mentored numerous students through his work at George Mason University and has supervised research in computational fluid dynamics, structural mechanics, and related fields. His research has been supported by various grants from government agencies and industry partners, enabling the development of advanced simulation tools applied in aerodynamics, hydrodynamics, shock-structure interaction, and medical applications. His codes and methods have been widely adopted in industry and academia for applications ranging from aircraft and ship design to medical simulations and urban pathogen transmission modeling. Loehner leads the Center for Computational Fluid Dynamics at George Mason University, which focuses on developing cutting-edge computational methods for fluid dynamics and related multiphysics problems. The center works on strategic application areas including blast mitigation, ship hydrodynamics, blood flow simulation, and pedestrian movement modeling. As a TUM-IAS Fellow, he collaborates with the Chair of Computational Modeling and Simulation at TUM on adjoint-based system identification of large-scale structures, bringing together expertise in computational mechanics and digital twin technology to address complex engineering challenges.
Kyusang Lee is an Associate Professor in the Electrical and Computer Engineering and Materials Science and Engineering departments at the University of Virginia. His research focuses on optoelectronic devices, neuromorphic computing, and smart sensors, emphasizing applications in solar energy conversion and flexible electronics. He holds a B.S. from Korea University (2005), M.S. from Johns Hopkins University (2009), and Ph.D. from the University of Michigan (2014). He conducted postdoctoral research at the University of Michigan and MIT. Education: B.S., Electrical Engineering, Korea University, 2005 M.S., Electrical and Computer Engineering, Johns Hopkins University, 2009 Ph.D., Electrical Engineering and Computer Science, University of Michigan, 2014 Postdoctoral Fellowships: University of Michigan (EECS), MIT (Mechanical Engineering) His research interests span thin-film and flexible optoelectronics, neuromorphic computing architectures, and AIoT-enabled smart sensors. Notable contributions include remote epitaxy techniques for semiconductor membrane integration and solar-tracking concentrator designs. His work bridges materials science and device engineering to advance energy-efficient optoelectronics and bioinspired systems. Key Research Themes: Organic/inorganic optoelectronic devices for solar energy Flexible and stretchable electronics Neuromorphic hardware for edge computing Gas sensing and bioinspired sensor systems Lee’s publications reflect interdisciplinary innovation, with recent work on ferroelectric transistors, neuromorphic vision systems, and high-efficiency photovoltaics. He received the NSF CAREER Award (2020) and AFOSR YIP Award (2023).
Nathalia Peixoto is an Associate Professor in the Department of Electrical and Computer Engineering and Affiliate Faculty in Bioengineering at George Mason University. Her work bridges neural engineering, biomedical applications, and assistive technology development with international collaborations across Israel, Ireland, Peru, and Korea. Educational background: PhD in Electrical Engineering, Universidade de Sao Paulo MS, University of Campinas Research Interests: Dr. Peixoto specializes in neural engineering with focus on brain-computer interfaces using wearable devices. Her lab develops: Neural prosthetics and implantable systems Bioimpedance-based medical sensors Low-cost electrophysiological recording platforms Community-centered engineering design solutions Publication Trends: Her 2022-2025 publications demonstrate strong interdisciplinary convergence between neuroscience, biomedical engineering, and AI. Key trends include machine learning for seizure detection in zebrafish models, electrochemical optimization of neural interfaces, and community-engaged design projects addressing societal challenges through transdisciplinary graduate training. Grants and Projects: Principal investigator for multiple NSF-funded initiatives: NRT-HDR: Transdisciplinary Graduate Training (2019-2024) Smart and Connected Communities: Networked Devices (2017-2019) Bioimpedance for retinal implants (2015-2017) C2MW: Classroom to Makers Week (2015-2016) Additional funding from VA STEM CoNNECT and Longwood University. Laboratory: The Neural Engineering Lab integrates chemistry, physics, and engineering disciplines through team-based projects involving high school to graduate students. Current work includes sustainable food-waste solutions, tremor-capturing robots for low-resource areas, and neural implants with international academic partnerships.
