جست‌وجوی استاد و پژوهشگر

Ana Lucicمشاهده پروفایل

استادیار

• Interpretable Machine Learning
• Artificial Intelligence
• Earth System Science
• +۳ مورد دیگر

Ana Lucic is an Assistant Professor in Artificial Intelligence at the University of Amsterdam , with a joint appointment between the Institute for Logic, Language and Computation and the Informatics Institute . Her research focuses on interpretable machine learning applications for scientific discovery and societal impact. Formerly at Microsoft Research AI for Science and Partnership on AI PhD in Explainable Machine Learning from University of Amsterdam (2022) BSc/MSc in Mathematics from McMaster University Research Highlights: Develops mechanistic interpretability methods for deep learning architectures. Created Aurora , a foundation model for Earth system forecasting outperforming traditional operational models in air quality prediction and tropical cyclone tracking. Pioneers Clifford-Steerable CNNs for geophysical data analysis. Actively hiring PhD students for AI transparency research . Collaborative Networks: Contributions to ELLIS Summer School and ICML workshops . Collaborates with Microsoft Research AI for Science team on climate-related ML projects. Involved in organizing TerraBytes workshop at ICML 2025. Recent Advancements: Key role in publishing Aurora model in Nature (2025), demonstrating superior performance in Earth system forecasting. Supervises Ege Erdogan , new PhD student focused on mechanistic interpretability. Actively contributes to open-source AI development through GitHub repositories and technical discussions.

• Embedded Systems
• Hardware/Software Co-design
• Energy-efficient AI Accelerators
• +۷ مورد دیگر

Oliver Bringmann is a full Professor and head of the Chair of Embedded Systems at the University of Tübingen, Germany, and a member of the board of directors at the FZI Research Center for Information Technology. His research integrates embedded-system design, energy-efficient AI accelerators, dependable automotive perception, and medical AI for capsule endoscopy. Education & Career Ph.D. in Computer Science, University of Tübingen, 2001 Diploma in Computer Science, University of Karlsruhe (KIT) Head, Chair of Embedded Systems, University of Tübingen (since 2012) Deputy spokesperson & spokesperson, Dept. of Computer Science, University of Tübingen (2014-2022) Board of Directors, FZI Research Center for Information Technology Research Interests Bringmann’s group pioneers hardware/software co-design for ultra-low-power Edge-AI , developing RISC-V based accelerators, compiler-aware neural-architecture search, and real-time perception systems for autonomous driving and medical devices. Key topics include: Energy-efficient AI architectures (“Edge AI”) and custom accelerator generation Robust collective perception under adverse weather (LiDAR, camera, V2X fusion) Timing/power-predictable embedded software and system-on-chip design automation Hardware-assisted security and safety for automotive & IoT systems AI-driven capsule endoscopy localization and anomaly detection Recent Publication Trends His 2024-2025 articles reveal a strong shift toward robust multimodal perception for automated driving (snow, fog, collective LiDAR fusion) and Edge-AI medical devices (capsule endoscopy with multi-task CNNs). Core contributions span dataset generation (SCOPE, SnowyLane), safety metrics (LSM), and fast performance modeling for DNN accelerators. Professional Service & Projects Executive/Steering Committees: IEEE/ACM DATE, CODES+ISSS, CASES, ITSS conferences EU CATRENE EDA roadmap chapter lead (Embedded Software & ESL-to-RTL) Principal investigator in Scale4Edge, OCEAN12, enerDAG and other national projects on energy-efficient sensorics and secure energy trading. His group maintains extensive collaborations with automotive and semiconductor industry, focusing on dependable, energy-aware embedded intelligence.

Kate Smithمشاهده پروفایل

استادیار

• Quantum Computing
• Computer Architecture
• Quantum Compilation
• +۵ مورد دیگر

Kate Smith is an Assistant Professor of Computer Science at Northwestern University, affiliated with the McCormick School of Engineering. She holds a PhD in Electrical Engineering from Southern Methodist University (SMU), along with MS and BS degrees in Electrical Engineering and Mathematics from the same institution. Her research focuses on quantum computing, specifically in system architecture, optimized compilation, error mitigation, and security. Prior to joining Northwestern in 2024, she worked at Infleqtion managing the Superstaq compiler team and as a postdoctoral scholar at the University of Chicago under the CQE/IBM program. She has contributed to over 25 peer-reviewed publications and served on technical committees for major conferences like MICRO, ISCA, and DAC. Education: SMU (PhD 2019, MS 2015, BS in EE/Math 2014). Professional experience includes roles at EPFL (Switzerland), Texas Instruments, and the Darwin Deason Institute for Cyber Security. Her honors include the 2022 HPCA Best Paper Award, MIT EECS Rising Star (2021), and the IEEE TC-MVL Early Career Award (2021). Research interests span quantum compilation, distributed systems, qudit processing, and quantum security. She co-organized the 2023 CCC Workshop on Next Steps in Quantum Computing and chaired the 2022 ISMVL conference. Her work emphasizes bridging hardware-software gaps to enable scalable quantum systems. Grants and collaborations include the EPiQC group at the University of Chicago and projects funded by the Swiss NSF. Teaching experience includes courses on quantum computing fundamentals and digital design at SMU, University of Chicago, and adjunct roles.

Risi Kondorمشاهده پروفایل

دانشیار

• Machine Learning
• Graph Theory
• Quantum Mechanics
• +۶ مورد دیگر

Risi Kondor is an Associate Professor in the Departments of Statistics and Computer Science at the University of Chicago. His research focuses on machine learning, group theory applications, and equivariant neural networks. He develops algorithms respecting geometric and physical symmetries, with contributions to graph learning, quantum mechanics modeling, and multiresolution matrix factorization. Key projects include the development of Covariant Compositional Networks (CCNs) for graph-structured data and N-body networks for molecular simulations. He has created software tools like GraphFlow, SnOB (FFT for symmetric groups), and Mondrian for high-performance computing. His work bridges algebraic methods (e.g., Fourier analysis on permutation groups) with machine learning, addressing challenges in multi-object tracking, computer vision, and materials science. Risi Kondor holds grants including a DARPA Young Faculty Award ($500K, 2016–2018) and NSF funding for non-commutative harmonic analysis in machine learning. His research emphasizes theoretical foundations and practical applications, advancing areas like equivariant architectures, multiscale analysis, and symmetry-aware machine learning systems.

• Energy Informatics
• Building Energy Simulation
• Multi-Scale Energy Modelling
• +۴ مورد دیگر

James O'Donnell is an Associate Professor in the School of Mechanical and Materials Engineering at University College Dublin (UCD). He joined UCD in 2013 after working at Lawrence Berkeley National Laboratory. His research focuses on multi-scale energy modeling, interoperable BIM solutions, and sustainable building design. He contributes to buildingSMART’s Energy Modelling Expert Panel and teaches modules on building physics and energy analysis. Education: B.E. Civil and Environmental Engineering, University College Cork Ph.D. in Holistic Building Performance Analysis, University College Cork Professional Diploma in University Teaching & Learning, University College Dublin Research Interests: James specializes in energy informatics, building energy simulation, and semantic web technologies for the built environment. His work bridges BIM integration with energy modeling to optimize urban-scale performance. Grants & Funding: PROBONO (2022–2026): Integrator-centric approach for energy-efficient buildings NexSys (2022–2026): Next-Generation Energy Systems ALIVE (2020–2024): Indoor Environmental Quality in Energy-Efficient Buildings Teaching & Editorial Roles: James coordinates courses like Energy Systems in Buildings and serves as an Associate Editor for Journal of Building Engineering and editorial board member of Advanced Engineering Informatics . Professional Affiliations: Member of Engineers Ireland, International Building Performance Simulation Association (IBPSA), and European Council on Computing in Construction.

Jonathan Gammellمشاهده پروفایل

استادیار

• Autonomous Systems and Control
• Robotics
• Artificial Intelligence
• +۴ مورد دیگر

Jonathan Gammell is an Assistant Professor at Queen's University's Department of Electrical and Computer Engineering and a member of the Ingenuity Labs Research Institute. He holds an adjunct fellowship at the Oxford Robotics Institute (University of Oxford), where he previously taught. His expertise spans autonomous systems, robotics, and AI, with a focus on motion planning for diverse applications, including medical devices, self-driving cars, and aerial robotics. He leads the Estimation, Search, and Planning (ESP) group, which develops algorithms for autonomous systems. Educations: BASc in Mechanical Engineering and Physics from the University of Waterloo MASc and PhD in Robotics from the University of Toronto Institute for Aerospace Studies Research Interests: Dr. Gammell's work addresses fundamental motion planning challenges in robotics, emphasizing asymptotically optimal algorithms like BIT*, AIT*, and FCIT*. His research integrates robotics with medical applications (e.g., knee replacement implants) and autonomous systems (e.g., aerial mapping for emergency response). His algorithms are widely adopted in industry and academia, including collaborations with NASA JPL and Oxford orthopaedic surgeons. Publications: His recent work explores advanced motion planning frameworks (e.g., AORRTC, Osprey), multimotion visual odometry (MVO), and medical robotics applications. These contributions highlight his dual focus on theoretical algorithm development and practical, high-impact applications. Awards: CIPPRS Award for Best Canadian PhD Thesis (Medical Imaging/Robotics) NASA Group Achievement Award (2021, 2022) IROS Best Paper Shortlist (2020) Advising & Grants: He advises on projects involving autonomous systems, medical robotics, and AI. His grants support collaborations with industry and academic partners, including NASA JPL and Oxford's robotics and medical teams. The ESP group's open-source tools (e.g., Planner Developer Tools) enable reproducible motion-planning research. Labs/Teams: He leads the ESP research group at Queen's University and collaborates with the Oxford Robotics Institute and Ingenuity Labs, focusing on cutting-edge robotics solutions for real-world challenges.

Zhen Liمشاهده پروفایل

استادیار

• Multiscale/Multiphysics modeling
• Soft matter and Smart materials
• Biophysics and Collective dynamics
• +۷ مورد دیگر

Dr. Zhen Li is an Assistant Professor in the Department of Mechanical Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. He joined Clemson in August 2019 after serving as a research associate professor at Brown University and a postdoctoral research associate at University of California, Merced. Education: Ph.D. in Fluid Mechanics, Shanghai University, 2012 MS in Fluid Mechanics, Shanghai University, 2008 BS in Engineering Mechanics, Wuhan University, 2005 Dr. Li's research focuses on multiscale modeling of soft matter, complex fluids, biophysics, and collective dynamics using both bottom-up (coarse-grained molecular modeling) and top-down (from continuum descriptions to fluctuating hydrodynamics) approaches, along with high-performance computing. His work spans mathematical theory for coarse-graining and model reduction, statistical methods and machine-learning approaches applied to multiscale modeling, memory effects in complex fluids, and concurrent coupling of heterogeneous solvers for scale-bridging. Analysis of Dr. Li's recent publications reveals a strong trend toward integrating machine learning with traditional computational methods, particularly neural operators for multiscale problems. His work spans diverse applications from bubble dynamics and blood flow to materials science and bioprinting, demonstrating the versatility of his computational approaches across multiple disciplines in engineering and physics. Awards and Recognition: CECAS Dean's Professor Award (2024) Award of Excellence - Junior Faculty (2021-2022) Best Research Poster Award at SC19 (2019) 2nd Place Award of Best Poster Presentation at DOE/EFRC AIM for Composites meeting (2024) Dr. Li actively mentors PhD students including Miles Lu, Ryan Wan, Haizhou Wen, and Ali Mohammadi, who have published significant research in computational mechanics. His research is supported by multiple grants including an NSF Elements grant as PI for 'SciMem: Enabling High Performance Multi-Scale Simulation on Big Memory Platforms', an NSF CDS&E grant as co-PI for 'HAM3R: Heterogeneous Automated Management of Multiscale Methods and Resources', a DOE/EFRC grant as Thrust lead co-PI for 'AIM for Composites', and a NASA EPSCoR grant as Science-PI. Dr. Li leads the MuthComp (Multiscale theory and Computation) research group, which focuses on developing interfaces between Engineering, Applied Mathematics, Physics-based Machine Learning, and High Performance Scientific Computing. The group has active collaborations with institutions including Idaho National Laboratory, University of Tokyo, and Brown University, and has developed open-source software including USERMESO for GPU-accelerated DPD simulations.

Aydin K. Sunolمشاهده پروفایل

استاد

• Green Engineering
• Supercritical Fluid Technology
• Systems Engineering
• +۷ مورد دیگر

Dr. Aydin K. Sunol is a Professor of Chemical, Biological and Materials Engineering at the University of South Florida's College of Engineering. With a distinguished career spanning several decades, he leads the Environmentally Friendly Engineered Systems (EFES) Lab and serves as principal investigator for numerous research projects funded by NASA, DOD, NSF, and industry partners. Education: PhD in Chemical Engineering, VPI & SU, Blacksburg, Virginia MEng in Industrial Engineering and Operational Research, VPI & SU, Blacksburg, Virginia Diploma in Chemical Engineering, University of Aston, Birmingham, England BS in Chemical Engineering, Bogazici University, Istanbul, Turkey Dr. Sunol's research focuses on green engineering and chemistry, sustainability in the chemical industry, systems engineering, and cleaner energy conversion processes. His work integrates global computational methods, machine learning, product prototyping, and experimentation across multiple temporal and spatial scales. Recent projects include developing nano-structured photo-catalysts, therapeutic particles, and energetic materials using environmentally friendly pathways, as well as creating efficient fuel conversion processes utilizing supercritical fluids. His research demonstrates a consistent trend toward sustainable solutions that address fundamental challenges in materials science, energy conversion, and environmental protection through innovative application of supercritical fluid technology and computational methods. Scientific Awards: USF Chemical and Biomedical Engineering Department Outstanding Teaching Award, 2018 University of South Florida Outstanding Undergraduate Teaching Award, 2002-2003 Engineering Professor of the Year, 1984 Outstanding Professor of Chemical Engineering, VPI & SU, 1981 Outstanding Technology Innovation Award, Aerospace Space System Conference, 2010 As an educator, Dr. Sunol has advised 18 PhD and 29 Master's students, developing innovative teaching methods and curricula including NSF-funded Web-based Teaching Modules and Design Course Series. His research has been supported by diverse funding sources including DOE, NSF, NATO, UNESCO, NAVY, NASA, and numerous industry partners. Dr. Sunol also serves as principal partner and CTO of Temptroll LLC and Accent Creations LLC, which develop self-heating and cooling products, demonstrating the practical application of his research. The EFES Lab, which Dr. Sunol directs, brings together interdisciplinary researchers including chemical engineers, mechanical engineers, and computer scientists to tackle complex challenges in sustainable engineering. Current team members include PhD candidates working on nano-structured materials, wastewater management systems, and temperature modulation products, continuing the lab's tradition of innovation in environmentally friendly engineered systems.

Yazhou Tuمشاهده پروفایل

استادیار

• Cyber-Physical System Security
• Side Channel Analysis
• Sensing and Automation Security
• +۳ مورد دیگر

Yazhou Tu is an Assistant Professor in the Department of Computer Science and Software Engineering at Auburn University. He specializes in Cyber-Physical System Security, Side Channel Analysis, and Privacy in embedded systems. His work focuses on addressing vulnerabilities in sensors, actuators, and IoT devices to enhance security and privacy in critical systems. Education: Ph.D. Computer Science, University of Louisiana at Lafayette M.S. Software Engineering, Tsinghua University B.S. Software Engineering, Wuhan University Research Interests: Dr. Tu’s research explores cutting-edge topics such as adversarial control of embedded systems, acoustic and magnetic side-channel attacks, and privacy risks in robotic and biomedical systems. He develops novel defense mechanisms like ADC-Bank and Transduction Shield to counteract physical signal injection attacks. Key Contributions: His work spans sensor security, IoT exploitation, and healthcare technology, with notable studies on 3D printer IP theft, vehicle control via smart glasses, and keystroke tracking via audio. He also investigates physics-informed ML for porous media and glucose monitoring systems. Awards & Grants: No specific awards or grants listed in the provided data, but his prolific publication record highlights sustained research excellence. Labs & Collaborations: Engaged in Auburn’s Center for Artificial Intelligence and Cybersecurity Engineering and other interdisciplinary initiatives focused on secure embedded systems and cyber-physical infrastructure.

Ali Khosraviمشاهده پروفایل

استادیار

• Bioinspired Geotechnics
• Thermo-hydro-mechanical Processes
• Soil-Structure Interaction
• +۳ مورد دیگر

Ali Khosravi is an Assistant Professor in the Department of Civil and Environmental Engineering at Auburn University, specializing in bioinspired geotechnics and geomechanics. His work integrates advanced materials science, remote sensing, and computational modeling to address challenges in infrastructure resilience, climate change impacts, and sustainable geotechnical systems. He holds a Ph.D. from the University of Colorado Boulder and degrees from Sharif University of Technology. Research focuses include: Thermo-hydro-mechanical processes in soils and fractured rocks Soil-structure interaction for seismic and environmental applications Robotics-inspired probes for subsurface exploration Permafrost degradation under climate change Energy geosystems and geothermal applications Recent projects involve collaborating with the National Center for Additive Manufacturing Excellence (NCAME) to develop underground installation robots, assessing Arctic permafrost stability with multi-disciplinary teams, and using InSAR remote sensing to monitor infrastructure like levees and highways. His research bridges geotechnical engineering with emerging technologies like machine learning and bioinspired design. Grants and collaborations include Auburn Engineering’s Kiemyung University travel grant for international research partnerships. His lab focuses on innovative solutions for geohazards, energy efficiency, and smart infrastructure systems.

Van Anh Huynh-Thuمشاهده پروفایل

استادیار

• Machine Learning
• Bioinformatics
• Gene Regulatory Network Inference
• +۳ مورد دیگر

Vân Anh Huynh-Thu is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Liège (Belgium). Her research focuses on improving machine learning techniques with an emphasis on model interpretability. She is based at B28: Systems and Modeling in Quartier Polytech, Allée de la Découverte 10, 4000 Liège, Belgium. Her primary research interests span Machine Learning , Bioinformatics , and Gene Regulatory Network Inference . Dr. Huynh-Thu has developed several influential methods including GENIE3, dynGENIE3, and Jump3 for inferring gene regulatory networks from expression data. Her work bridges the gap between machine learning theory and biological applications, particularly in understanding complex disease mechanisms through computational approaches. She has made significant contributions to interpretable machine learning models that maintain high predictive accuracy while providing insights into feature importance and model behavior. Her research demonstrates a progression from purely computational methods toward translational applications in medical research. Analysis of her recent publications reveals a clear trajectory from foundational work on gene regulatory network inference toward broader applications in medical research, particularly in Crohn's disease. Her research increasingly integrates machine learning with clinical applications, demonstrating a shift from purely computational methods to translational research with direct medical implications. The consistent emphasis across her work is on interpretability, rigorous validation, and the application of tree-based methods to complex biological systems, with a growing focus on proteomics and biomarker discovery for inflammatory bowel diseases. Dr. Huynh-Thu maintains an active GitHub presence with implementations of her methods, demonstrating her commitment to open science and reproducibility. Her software repositories have garnered significant attention from the research community, with GENIE3 alone having 88 stars and 37 forks on GitHub. She has developed multiple implementations of her algorithms in Python, MATLAB, and R, making them accessible to researchers across different computational environments. Her work has been influential in the DREAM challenges, where GENIE3 was the best performer in two network inference competitions.

Ralph Detersمشاهده پروفایل

استاد

• Multi-Agent Systems
• Scalability & Dependability of Distributed Systems
• Mobile & Ubiquitous Computing
• +۴ مورد دیگر

Ralph Deters is a Professor in the Department of Computer Science at the University of Saskatchewan , where he co-leads the MADMUC (Multi-Agent Distributed Mobile and Ubiquitous Computing) Lab . He has taught courses including CMPT 298, CMPT 350, CMPT 436, CMPT 842, and CMPT 898, and served as co-coordinator for CMPT 400/CMPT 405. Education Ph.D. in Computer Science, Federal Armed Forces University Munich (1998) MS (Diploma) in Computer Science, Technical University Berlin (1992) BS in Computer Science, Technical University Berlin (1988) Research Interests focus on multi-agent systems , scalable distributed systems , mobile/ubiquitous computing , and privacy-preserving technologies . Current lab projects include: Middleware infrastructures for decentralized systems (cloud/fog/mobile) Social software for sustainable user collaboration Decentralized trust-based data storage Persuasive technologies for health, physical activity, and learning Ethical AI and dark patterns in UI design Blockchain applications in IoT and access control Publications highlight trends in digital twins , blockchain integration , synthetic data for privacy , and machine learning for social impact , with recent work spanning health analytics, agricultural AI, and smart city applications. Lab Impact : The MADMUC Lab, established in 2000 with a $895,687 CFI grant, has graduated 120 researchers (17 PhD, 103 MSc), including two Vanier Scholars. The lab emphasizes awards, scholarships, and interdisciplinary collaboration with 152 total alumni.

Chao Liمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence in Healthcare
• Medical Imaging
• Precision Medicine
• +۳ مورد دیگر

Chao Li is a Principal Research Fellow at the University of Cambridge's Faculty of Mathematics, specializing in AI-driven healthcare solutions. His research focuses on precision medicine through image-based AI and multi-omics integration, with applications in neurological disease modeling, surgical oncology, and clinical AI safety. He leads the Centre for Mathematical Imaging in Healthcare , advancing translational AI for personalized medicine. Research Themes: Image-based AI for precision mental health AI in surgical and interventional oncology Multi-omics approaches for disease characterization Evaluation of AI innovations for clinical translation Publications highlight advancements in multimodal fusion for diagnostics, histology-molecular marker integration, and neuroimaging analytics for mental health. His work bridges computational mathematics with clinical challenges, emphasizing real-world healthcare impact. Labs/Teams: Active member of the Centre for Mathematical Imaging in Healthcare , collaborating across departments to develop AI tools for clinical deployment.

• AI & Neuroscience
• Biomedical Engineering
• Neurotechnology
• +۷ مورد دیگر

Aldo Faisal is a Professor of AI & Neuroscience at Imperial College London's Department of Bioengineering and Faculty of Engineering. He is also the Founding Director of the UKRI Centre for Doctoral Training in AI for Healthcare (£20M), leading the Faisal Lab. His roles include associate investigator at the MRC London Institute of Medical Sciences and affiliation with the Gatsby Computational Neuroscience Unit (UCL). His research bridges AI, neuroscience, and healthcare, focusing on neurotechnology, human behavior analysis, and clinical applications. Education: Studied Computer Science and Physics in Germany, followed by Biology at the University of Cambridge (Emmanuel College). Earned a PhD in Neuroscience under Simon Laughlin, and postdoctoral work with Daniel Wolpert on sensorimotor control. Prior professional experience includes roles at McKinsey & Co. and Credit Suisse. Research Interests: Combines cross-disciplinary approaches to study brain-behavior relationships, developing technologies for neurological disorders and amputees. Key labs include the Brain & Behaviour Lab (neurotechnology) and Behaviour Analytics Lab (behavioral data science). Techniques include machine learning, robotics, and neuroimaging (EEG/fNIRS). Publications: Over 100+ peer-reviewed articles, spanning AI in healthcare, motor learning, and neurotechnology. Recent work emphasizes safety of AI in critical care, wearable biomarkers for neuromuscular diseases, and human-AI collaboration. Awards: UKRI Turing AI Fellowship, Toyota Mobility Prize ($50k), Rosetree Interdisciplinary Award (£300k), and fellowships from German National Merit Foundation and Böhringer-Ingelheim Foundation. Elected to Global Futures Council (WEF, 2016). Grants & Labs: Leads £20M CDT in AI for Healthcare, £2M Turing Fellowship project, and manages labs at Imperial and University of Bayreuth (Germany). Collaborates with institutions like CRUK Convergence Science Centre and Data Science Institute.

• Chemical Engineering
• Materials Science
• Nanotechnology
• +۳ مورد دیگر

Prof. Robert Grass is a Lecturer at the Department of Chemistry and Applied Biosciences at ETH Zurich, affiliated with the Institute for Chemical and Bioengineering Sciences. His research focuses on innovative applications of nanotechnology, DNA-based storage systems, and sustainable catalytic processes for CO2 valorization. Grass has pioneered silica-encapsulated DNA technologies for traceability in healthcare, environmental monitoring, and anti-counterfeiting measures. His work bridges chemical engineering with information technology, addressing challenges in long-term data preservation and molecular-level security. Current projects include developing compostable DNA storage materials and designing catalysts for methanol synthesis from CO2, contributing to both environmental sustainability and energy systems. Grass's interdisciplinary approach integrates nanomaterials design, enzymatic processes, and machine learning to advance next-generation storage and sensing technologies. Research Interests: Development of DNA-based storage systems with error-correction mechanisms Nanoparticle engineering for medical and environmental applications Catalytic materials for CO2 conversion and green chemistry Bio-inspired security systems using molecular randomness Sustainable materials for long-term data preservation His recent work highlights advancements in silica-encapsulated DNA tracers for tracking pathogen transmission dynamics, as well as low-nuclearity catalysts enabling efficient methanol synthesis from CO2. Grass actively explores the intersection of nanotechnology and digital information, including cryptographic applications leveraging DNA's inherent complexity.