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Possu HuangView profile

Assistant Professor

• Protein Design
• Computational Biology
• Structural Biology
• +4 more

Possu Huang serves as Assistant Professor of Bioengineering in the Department of Bioengineering at Stanford University's School of Engineering. His research group operates from the Shriram Center for Bioengineering, sharing laboratory space with the Bintu Lab while maintaining an active interdisciplinary program bridging computational and experimental protein engineering. His educational journey includes: B.A. in Molecular and Cell Biology (Biochemistry) from UC Berkeley Ph.D. in Biochemistry and Molecular Biophysics from Caltech Postdoctoral training as Senior Fellow at University of Washington Dr. Huang's research focuses on atomic-precision protein design through integrated computational-experimental approaches. His lab has pioneered breakthroughs including the first computationally designed protein-protein interface, TIM barrel fold design principles, and the eOD HIV immunogen. Current work leverages machine learning , structural biology , and library optimization to develop therapeutic proteins and nanotechnology platforms, with particular emphasis on achieving natural-level complexity in engineered systems. Analysis of his 2023-2025 publications reveals three dominant trends: (1) generative AI for all-atom protein design (e.g., Protpardelle, SHAPES), (2) immunology applications targeting MHC complexes for HIV vaccine development, and (3) optogenetic tools through engineered fluorescent proteins. These works consistently integrate deep learning with wet-lab validation to solve biomedical challenges. Dr. Huang has mentored nine graduate students to completion, including PhD candidates Carla (defended June 2025), Christian (defended May 2025), and master's students Shankara and Kirsten. His lab maintains active social media presence (@possuhuanglab) and regularly publishes high-impact research while training the next generation of protein engineers. The Huang Lab operates as a dynamic interdisciplinary hub where computational biologists collaborate with experimentalists to push the boundaries of protein design. Current projects include developing tetravalent nanobody platforms, photoswitchable binders for temporal protein control, and AI-driven solutions for HIV immunogen design, all conducted within Stanford's state-of-the-art bioengineering facilities.

Zachary E. RossView profile

Professor

• Seismology
• Geophysics
• Machine Learning
• +7 more

Zachary E. Ross is a Professor of Geophysics at the California Institute of Technology (Caltech) and holds the William H. Hurt Scholar distinction since 2021. His research integrates machine learning, computational mathematics, and seismology to analyze earthquakes and fault zones using large seismic datasets. Education B.S., University of California, Davis (2009) M.S., California Polytechnic State University, San Luis Obispo (2011) Ph.D., University of Southern California (2016) His research focuses on high-resolution imaging of fault zones, understanding earthquake sequences in space and time, and applying artificial intelligence to seismic data analysis. He develops scalable algorithms for waveform inversion, ground-motion synthesis, and real-time seismic monitoring. Recent publications highlight his work on neural operators for wave propagation, AI-driven seismicity analysis, and induced earthquake dynamics. He teaches advanced courses including Ge 264 – Machine Learning in Geophysics and Ge 271 – Dynamics of Seismicity . Scientific Awards William H. Hurt Scholar (2021–present)

Mohamed FarhatView profile

Researcher

• Cavitation & Multiphase flows
• Flow Induced Noise & Vibration
• Fluid-Structure Interaction
• +5 more

Mohamed Farhat is a Senior Scientist at EPFL's School of Engineering, Department of Mechanical Engineering, where he leads the Research Group on Cavitation and Interface Phenomena. He serves as PhD Director, Lecturer, and Member of EPFL Doctoral Committee (Mechanics), while also representing EPFL at CLUSER association and coordinating activities at the Société Hydrotechnique de France (SHF). His research expertise spans Cavitation & Multiphase flows, Flow Induced Noise & Vibration, Fluid-Structure Interaction, Flow control, Flow instabilities in hydro turbines and pumps, Condition monitoring of Hydraulic Machines, Hemodynamics, and Advanced Instrumentation in Fluid Dynamics. Farhat's work uniquely bridges fundamental fluid mechanics with practical applications across hydropower, marine propulsion, healthcare, and water management sectors. Analysis of his recent publications reveals strong focus on cavitation bubble dynamics, with particular emphasis on measurement techniques for collapsing bubbles, vortex shedding control, hydrodynamic monitoring of hydraulic machinery, and biomedical applications of cavitation phenomena. His work increasingly integrates advanced imaging techniques with computational modeling to understand complex multiphase flow phenomena. 2021: Life Sciences Book Award of the International Academy of Astronautics 2019: 1st Prize Winner of Scientific Image Contest (Swiss National Science Foundation) 2020: EPFL-Rhyming Prize (Best PhD thesis in Fluid Mechanics) 2018: EPFL-EDME Prize (Best PhD thesis in Mechanics) 2015: Edmund Optics Educational Award 2014: APS-DFD Gallery of Fluid Motion Award Farhat has successfully supervised numerous PhD students including Ali Amini, Philippe Ausoni, and Outi Supponen, with research spanning from fundamental bubble dynamics to practical hydraulic machinery applications. His Cavitation Research Group maintains strong collaborations with industry partners in hydropower and medical device sectors. Current research directions include advanced instrumentation for cavitation monitoring, condition-based maintenance of hydraulic machinery, and biomedical applications of cavitation phenomena in therapeutic ultrasound and drug delivery.

Simon MooreView profile

Professor

• Computer Architecture
• Computer Security
• Hardware Security
• +3 more

Simon Moore is a Professor of Computer Engineering at the University of Cambridge's Department of Computer Science and Technology. He leads the Computer Architecture research group, focusing on secure processors and subsystems, particularly the CHERI project. His work emphasizes formal verification, hardware-software co-design, and scalable security solutions. He is a Fellow and Director of Studies at Trinity Hall, overseeing undergraduate admissions and mentoring in Computer Science. Research Interests: Moore's primary focus is the CHERI secure processor architecture, integrating RISC-V cores with formal verification. His work spans secure hardware design, memory safety, and embedded systems. Notable contributions include the CHERI-RISC-V microarchitecture, CheriABI, and formal verification frameworks. Key Projects: CHERI, CheriBSD, Morello (ARM collaboration) Recent Achievements: Test of Time Award (IEEE Security & Privacy 2025), finalist for Bhattacharyya Award (2022) Grants: Innovate UK Digital Security by Design, DARPA Mission Oriented Resilient Clouds Publications: Over 200 papers on secure architectures, including influential work on CHERI's capability model, formal verification, and hardware security. Recent focus areas include temporal memory safety, embedded system security, and GPU-based capability systems. Labs/Teams: Directs the Computer Architecture Group and collaborates with industry partners like ARM and Microsoft on CHERI implementations.

Kieron BurkeView profile

Professor

• Density Functional Theory (DFT)
• Quantum Mechanics
• Materials Science
• +3 more

Kieron Burke is a Distinguished Professor in the Department of Chemistry and Department of Physics at the University of California, Irvine (UCI), where he also leads the Burke research group. His academic contributions focus on advancing density functional theory (DFT), a cornerstone of computational quantum mechanics. He collaborates with institutions like Google Accelerated Science and DeepMind to integrate machine learning into DFT, enhancing its predictive power for materials and chemical systems. His research spans theoretical and computational physical chemistry, materials science, and quantum mechanics. Notable achievements include pioneering density-corrected DFT and exploring DFT applications in extreme conditions like planetary interiors and fusion reactors. Prof. Burke's work is internationally recognized, with over 25,000 annual citations, and he holds prestigious fellowships from the American Physical Society and British Royal Society of Chemistry. Prof. Burke's educational initiatives include teaching a popular graduate course on machine learning for scientists and advocating for interdisciplinary training. His research group includes students from chemistry, physics, math, computer science, and engineering, reflecting his belief in cross-disciplinary approaches to scientific challenges. Awards: Fellow of the American Physical Society, British Royal Society of Chemistry, AAAS; Member of International Academy of Quantum Molecular Sciences Labs/Teams: Burke Research Group, focusing on DFT development and machine learning applications

Jonathan WeareView profile

Professor

• Monte Carlo sampling
• Multiscale analysis
• Randomized numerical linear algebra
• +2 more

Jonathan Weare is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He holds affiliations with the Faculty of Arts and Science and the Graduate School of Arts and Science. His academic journey includes roles as an Associate Professor at the University of Chicago (2014–2019) and Assistant Professor (2011–2014), following postdoctoral work as a Courant Instructor at NYU. He earned his Ph.D. in Mathematics from UC Berkeley in 2007. His research focuses on stochastic algorithms and models, with applications in astrophysics, biophysics, computational chemistry, and climate science. Key areas include Monte Carlo methods, rare event simulation, and machine learning-driven scientific analysis. Collaborations with domain experts ensure his work addresses real-world challenges in diverse fields. Recent publications emphasize advancements in trajectory stratification, rare event prediction using machine learning, and efficient algorithms for high-dimensional problems. Notable contributions include the BAD-NEUS framework and AI-based solar system instability predictions. His group’s interdisciplinary approach bridges computational methods with scientific inquiry. Weare has advised numerous students and mentored postdocs, fostering talent in applied mathematics and computational science. His work on Mercury’s orbital dynamics and extreme weather prediction showcases the societal impact of his research. Current projects explore AI applications in weather modeling and rare event analysis, leveraging cutting-edge machine learning techniques. Labs/Teams: His research group at Courant develops stochastic algorithms and collaborates with interdisciplinary teams in computational chemistry, climate science, and astrophysics. Key collaborations include the University of Chicago and Columbia University.

Qiu, JingjingView profile

Associate Professor

• Advanced Manufacturing
• Nanomaterials Synthesis
• Multifunctional Composites
• +3 more

Dr. Jingjing Qiu is an Associate Professor in the Department of Mechanical Engineering at Texas A&M University (TAMU), leading the Advanced Materials & Manufacturing (AM²) Lab. Her research focuses on advanced manufacturing, nanomaterials, multifunctional composites, sustainable materials, energy harvesting, and healthcare applications. She holds a Ph.D. in Industrial & Manufacturing Engineering from Florida State University (2008), and M.S./B.S. degrees in Materials Science from Beihang University (2004/2001). Prior to academia, she gained 1 year of industrial experience as a Quality Engineer at SAIC Motor. Research interests include AI-driven nanomaterials synthesis, low-carbon manufacturing processes, and biomedical innovations such as drug delivery systems for brain tumors. The AM² Lab emphasizes interdisciplinary collaboration in materials science, data science, and sensor integration for energy and medical devices. Recent work highlights include AI-assisted microplastics removal, thermoelectric energy harvesting via graphene aerogels, and neuromorphic computing systems. Her team actively pursues postdoctoral and PhD candidates for research in energy/healthcare materials, requiring expertise in nanomaterials characterization (e.g., SEM, XPS, electrochemical techniques) and interdisciplinary problem-solving. Lab facilities support cutting-edge fabrication and testing of functional materials. Publications span over 15 years, with recent trends in sustainable manufacturing, soft robotics, and bio-inspired materials. Ongoing projects include DOE-funded initiatives on rare earth recycling and low-carbon ceramic production. No scientific awards are explicitly listed in the provided texts.

Joshua LoftusView profile

Professor

• Algorithmic Fairness
• Causal Inference
• Data Science
• +4 more

Joshua Loftus is a Professor of Statistics and Data Science at the London School of Economics (LSE), Department of Statistics. His research focuses on improving data science practices to reduce bias and enhance fairness in algorithms, particularly addressing social harms and scientific reproducibility. He develops methods for statistical inference post-model selection and uses causality to analyze algorithm fairness and interpretability. His work bridges high-dimensional statistics, causal inference, and ethical AI, with a strong emphasis on practical applications using R in data science education. Before joining LSE, Loftus earned his PhD in Statistics at Stanford University, served as a Research Fellow at the Alan Turing Institute (affiliated with the University of Cambridge), and was an Assistant Professor at New York University (2017–2020). His research interests extend to the societal implications of technology, advocating for systems that prioritize human values over technical efficiency. Key research themes include counterfactual fairness, causal reasoning in algorithmic systems, and disaggregated interventions to reduce inequality. His recent work explores temporal aspects of fairness, model-agnostic auditing, and the integration of ethical frameworks into machine learning pipelines. While no scientific awards are explicitly listed, his contributions to foundational AI ethics and statistical methodology are widely recognized in academic circles. Advising and grant details are not provided in the source text, but his leadership in interdisciplinary research collaborations, such as the Turing Institute affiliation, highlights active engagement in research networks. Loftus is part of the LSE’s vibrant data science community, contributing to both theoretical advancements and applied solutions for equitable technology deployment.

• Quantum Theory of Condensed Matter
• Topological Insulators
• Spin Phenomena
• +7 more

Prof. Jelena Klinovaja is a Professor in the Department of Physics at the University of Basel, affiliated with the Philosophisch-Naturwissenschaftliche Fakultät. She holds leadership roles in research groups focusing on quantum theory of condensed matter, topological systems, and spin phenomena. Her career includes a PhD from the University of Basel (2012), a Harvard Fellowship (2013), and tenure as an assistant (2014) and associate professor (2019) before her current rank. She leads research on topological insulators, graphene, and Majorana fermions, with applications to topological quantum computing. Notable awards include the Swiss Physical Society Prize (2013) and an ERC Starting Grant (2017). Her work combines theoretical physics with experimental collaborations, particularly in nanowires and superconducting systems. She mentors students in the Honors Track program and contributes to interdisciplinary initiatives like NCCR SPIN. Education: Bachelor/Master from Moscow Institute of Physics and Technology (2007-2009); PhD in Theoretical Physics from University of Basel (2012). Research Interests: Topological effects in condensed matter, spintronics, quantum transport, Majorana fermions, and cavity quantum electrodynamics. Publications span over 50 peer-reviewed articles since 2012, focusing on topics like Josephson junctions, topological superconductivity, and hybrid systems. Her work bridges theoretical models with experimental realizations, emphasizing practical applications in quantum technologies. Awards: Swiss Physical Society Prize (2013), ERC Starting Grant (2017). Active in the scientific community, she collaborates with institutions worldwide and advises PhD students in quantum physics and nanoscience. Her research group also explores magnonic systems and topological materials engineering.

Christian PlesslView profile

Professor

• High-Performance Computing
• Reconfigurable Computing
• FPGA Acceleration
• +3 more

Prof. Dr. Christian Plessl is a W3 Professor of High-Performance Computing at the Institute of Computer Science, University of Paderborn. He leads the Paderborn Center for Parallel Computing (PC²), a national HPC center within the NHR alliance. His roles include Director of PC², Board Member of the NHR association, and member of the Sonderforschungsbereich 901. Education: PhD (Dr. sc. ETH) in Computer Engineering, ETH Zürich (2006) MSc in Electrical Engineering, ETH Zürich (2001) Postdoc at ETH Zürich (2007–2011) Research Interests: Architecture and tools for high-performance parallel and reconfigurable computing, FPGA acceleration, quantum chemistry, scientific computing, adaptive systems, and energy-efficient HPC solutions. Key projects include EKI-App (FPGA-based neural networks), FPGA4XPCS (X-ray spectroscopy), and HighPerMeshes (unstructured grid frameworks). Publications: Over 100 peer-reviewed works, focusing on FPGA acceleration, HPC frameworks, and quantum computing. Recent trends emphasize energy-efficient neural networks, FPGA-based quantum computing, and scalable HPC algorithms. Awards: Best Paper Awards at HEART 2023, ReConFig 2012/2014 Paderborn University Research Awards (2018, 2009) SEW-EURODRIVE Student Award (2001) Grants & Projects: Principal investigator in DFG, BMBF, and EU-funded projects. Collaborates with AMD/Xilinx, Intel/Altera, and Fujitsu. Leads initiatives like PerficienCC (custom computing) and HighPerMeshes. Labs/Teams: Directs the High-Performance Computing group at PC², focusing on FPGA supercomputing and HPC infrastructure. Active in the NHR alliance for national HPC coordination.

I. Safak BayramView profile

Senior Lecturer

• Electric Vehicle Grid Integration
• Smart Grids
• Demand-side Management
• +7 more

I. Safak Bayram is a Senior Lecturer (Associate Professor) in the Department of Electronic and Electrical Engineering at the University of Strathclyde, Glasgow, UK. He joined Strathclyde in 2020 as a Chancellor's Fellow, following his role as an Assistant Professor and Scientist at Hamad Bin Khalifa University, Qatar. His research focuses on advancing sustainability and efficiency in intelligent power grids and transportation networks through system-level modeling, control, and management frameworks. Education: PhD in Electrical and Computer Engineering, North Carolina State University (2014) MSc in Telecommunications, University of Pittsburgh (2010) BSc in Electrical and Electronics Engineering, Dokuz Eylul University, Turkey (2007) His research interests center on the integration of electric vehicles (EVs), renewable energy, and energy storage systems into the grid to decarbonize transportation and electricity sectors. He specializes in smart charging, demand-side management, harmonics, power quality, and V2G technologies. His recent publications (2024–2025) emphasize experimental and modeling approaches to EV smart charging impacts on transformers, phase imbalance, and grid compatibility, reflecting a strong focus on real-world deployment and grid resilience. Scientific Awards: Best Paper Award, IEEE SmartGridComm (2024) Best Paper Award, IEEE Workshop on Renewable Energy and Smart Grid (2015) Best Paper Award, IEEE SmartGridComm (2018) Best Readings in Smart Grid Communications (2014) Adjunct Faculty Member Appointment (2020) Dr. Bayram is actively involved in research leadership and academic service. He has secured multiple research grants as Principal Investigator, including projects on V2G hubs and off-grid EV charging. He serves as an Associate Editor for IEEE Transactions on Transportation Electrification and IET Electrical Systems in Transportation, and has organized special issues and conferences such as IEEE SmartGridComm. He regularly delivers tutorials and participates in international conferences, contributing to the global smart grid and electrification community. Labs and Research Teams: His work is supported by active collaborations with industry (e.g., Arnold Clark Automobiles Limited) and research institutions. He leads research on modular EV charging (BumblebeeEV), smart charging algorithms, and grid integration projects, often involving experimental validation and field data analysis.

• Sanitary Engineering
• Wastewater Treatment
• Constructed Wetlands
• +7 more

Priv.Doz.DI Dr.nat.techn. Günter Langergraber is a Professor and Head of the Institute of Sanitary Engineering and Water Pollution Control at the University of Natural Resources and Life Sciences, Vienna (BOKU). He also serves as Deputy Head of the Department of Landscape, Water and Infrastructure since 2025. With expertise in nature-based solutions , constructed wetlands , and resource-oriented sanitation systems , his work integrates modelling and simulation with practical applications for wastewater treatment and urban sustainability. Habilitation in Sanitary Engineering (2012), BOKU PhD in Natural Sciences (2001), BOKU His research focuses on developing advanced wastewater treatment technologies using constructed wetlands , biochar systems , and smart sensors . Key areas include urban water circularity , stormwater management , and climate resilience through nature-based infrastructure . He leads major EU-funded projects like ProCleanLakes and UniNEtZ , emphasizing the role of sustainable sanitation in achieving SDG 6 . Recent publications analyze: Design optimization of vertical flow wetlands using numerical models Implementation frameworks for circular cities via nature-based solutions Hydrological dynamics in Mediterranean and tropical wetland systems Soil water balance quantification via stable isotopes Scientific accolades include IWA Fellow (2014) and the IWA Project Innovation Award (2011) . He has supervised 478 publications and 56 projects, including EU Horizon 2020 initiatives and Austrian Science Fund (FWF) programs. As a keynote speaker at international conferences, he advocates for renaturalized urban water systems and decentralized sanitation . Langergraber contributes to technical guidelines (e.g., DWA-A 272) and policy reports for Austrian ministries. His work bridges academic research , industry collaboration , and public engagement through media appearances and training programs for wastewater plant operators.

Ralf ZimmerView profile

Professor

• Bioinformatics
• Systems Biology
• Machine Learning
• +5 more

Ralf Zimmer is a Full Professor for Practical Informatics and Bioinformatics at Ludwig Maximilian University of Munich (LMU) since 2001, affiliated with the Department of Informatics in the Faculty of Mathematica, Informatics and Statistics. He concurrently serves as Head of Section III and Head of the Research Group Network Regulation and Modeling / Machine Learning at the Leibniz Institute for Food Systems Biology at TUM (Leibniz-LSB@TUM) in Freising, Germany. His academic foundation includes a Diploma with distinction in Computer Science, Applied Mathematics and Operations Research from the University of Bonn (1981-1986), followed by a summa cum laude Doctorate in Computer Science and Applied Mathematics from CAU Kiel in 1990, where he received dual honors: the CAU Dissertation Award and Best Dissertation Award. Zimmer's research pioneers the integration of bioinformatics, systems biology, and machine learning to decode molecular food-consumer interactions. His group develops causal system models for biological networks, validated through in silico simulations and multi-omics perturbation experiments (transcriptomics/proteomics). Core methodologies include network regulation modeling, algorithmic bioinformatics, and database construction linking food compounds to biochemical networks and cellular phenotypes, with translational goals for food and biotech innovation. His 14 most recent publications (2012-2024) reveal dominant trends in multi-omics immunology and cardiovascular research, featuring computational innovations for high-throughput data analysis. Key themes include host-pathogen dynamics (viral infections), inflammatory disease mechanisms (atherosclerosis), and methodological advances in proteomics/transcriptomics, consistently bridging fundamental bioinformatics with clinical applications. Major scientific recognitions include: CAU Dissertation Award and Best Dissertation Award (1990) Director of LMU's Informatics Department (2010-2012) DFG Review Board membership for biomedical foundations (2008-2016) Leadership of the DFG Bioinformatics Munich Center (2001-2008) Academic Senate election at LMU München (2011) Zimmer directs LMU/TUM's joint B.Sc./M.Sc. bioinformatics programs since 2001 as founding architect of the DFG-funded Bioinformatics Munich initiative. His educational leadership spans spokesperson roles for international training groups (IRTG RECESS), collaborative research centers (SFB1123 Atherosclerosis), and elite programs (Data Science, Munich Center for Machine Learning). Grant stewardship includes directing the DFG Bioinformatics Munich Center and shaping national funding policy via the DFG review board. At Leibniz-LSB@TUM, his research group pioneers databases connecting food compounds to cellular phenotypes through molecular networks, collaborating with Munich universities, clinics, and biotech partners to develop high-throughput sequencing/proteomics applications for future food and health innovations.

Arno OnkenView profile

Assistant Professor

• Data Science
• Machine Learning
• Computational Neuroscience
• +7 more

Dr. Arno Onken is a Lecturer (Assistant Professor) in Data Science for Life Sciences at the School of Informatics, University of Edinburgh, where he is also affiliated with the Institute for Adaptive and Neural Computation. He leads a research group focused on developing machine learning and statistical methods for modeling neural activity and analyzing large-scale neuroscience data. His work bridges artificial intelligence and computational neuroscience. His research interests lie at the intersection of machine learning, statistics, and neuroscience. He develops flexible probabilistic models such as copulas and Gaussian processes, deep learning architectures like Vision Transformers for brain activity prediction, and matrix/tensor factorization techniques for dimensionality reduction in neural datasets. His group aims to uncover interpretable structure in complex neural recordings and understand how behavior and cognition are encoded in population activity. The recent publications reflect a strong trend in combining modern deep learning with classical statistical modeling to analyze large-scale neural recordings. His work spans from foundational methods in copula modeling and information theory to applications in predicting visual cortex responses and modeling brainstem-hippocampus interactions across sleep states. The research has been published in top venues including NeurIPS, CVPR, eLife, and PLoS Computational Biology. Dr. Onken actively supervises PhD students and has developed several open-source scientific software packages, including the Mixed Vine Toolbox and Population Spike Train Factorization Toolbox. He teaches core courses in Machine Learning and Pattern Recognition and Data Mining and Exploration at the University of Edinburgh.

Steven CorcelliView profile

Professor

• Theoretical Chemistry
• Computational Chemistry
• Biophysical Chemistry
• +7 more

Steven Corcelli is a Professor and Interim Dean of the College of Science at the University of Notre Dame, with a joint appointment in the Department of Chemistry and Biochemistry. He has held key leadership roles including Department Chair (2022–2025) and Associate Dean for Interdisciplinary Studies (2019–2022). His research is centered in the Computational Molecular Science & Engineering Laboratory (CoMSEL), where he leads a team exploring molecular and biomolecular systems through advanced simulations. Education: Ph.D. in Chemistry, Yale University, 2001 Sc.B. in Chemistry, Brown University, 1997 His research interests bridge theoretical and experimental physical chemistry, with a strong emphasis on aqueous electrolytes, biomolecular binding, and vibrational spectroscopy . He employs GPU-accelerated molecular dynamics and enhanced sampling techniques to study ion transport in aqueous solutions and the mechanisms of protein-DNA and protein-protein interactions, particularly in immunological contexts such as T-cell receptor binding. His work has significant implications for battery technologies and drug design. The 15 most recent publications reveal a consistent focus on molecular simulation, biophysical chemistry, and spectroscopy , with recurring themes in DNA-ligand binding, ion solvation, and immune recognition. His group develops and applies novel computational methods, such as the coupled local mode approach, to interpret vibrational spectra with high accuracy. Scientific Awards: Thomas P. Madden Award (2020) Rev. Edmund P. Joyce, C.S.C. Award for Excellence in Undergraduate Teaching (2019, 2012) Fellow, American Chemical Society (2016) Sloan Research Fellowship (2009) NSF CAREER Award (2009) Camille and Henry Dreyfus New Faculty Award (2005) Kavli Fellow, National Academies of Science (2011) Corcelli has advised numerous graduate students and postdoctoral researchers, many of whom have gone on to successful careers in academia and industry. His lab collaborates extensively with experimental groups, particularly in spectroscopy and immunology. He has secured substantial research funding and published over 85 peer-reviewed articles. His lab, CoMSEL, benefits from high-performance computing resources through the Center for Research Computing at Notre Dame, enabling large-scale simulations of complex molecular systems. Research Labs and Teams: The Computational Molecular Science & Engineering Laboratory (CoMSEL) is a multidisciplinary research group that combines theoretical chemistry, biophysics, and materials science. The team includes graduate students and staff scientists working on projects ranging from fundamental solvation dynamics to applied biomedical simulations.