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Zhengyuan ZhouView profile

Assistant Professor

• Machine Learning
• Reinforcement Learning
• Stochastic Optimization
• +12 more

Zhengyuan Zhou is an Assistant Professor in the Department of Technology, Operations, and Statistics at the Leonard N. Stern School of Business, New York University. He is also associated faculty at the Department of Computer Science and Engineering, Tandon School of Engineering, and affiliated with the NYU Center for Data Science. He joined NYU Stern in 2020 after serving as an IBM Goldstine Research Fellow and a Visiting Scholar at NYU Stern during 2019–2020. Education: Ph.D., Electrical Engineering, Stanford University, 2019 Master’s in Computer Science, Stanford University Master’s in Statistics, Stanford University Master’s in Economics, Stanford University B.A., Mathematics, UC Berkeley B.S., Electrical Engineering and Computer Sciences, UC Berkeley His research centers on the intersection of machine learning, stochastic optimization, control theory, and game theory, with a focus on data-driven decision-making. He develops algorithms for reinforcement learning, contextual bandits, and policy learning under uncertainty, with applications in inventory control, revenue management, and auction bidding. His work emphasizes sample efficiency, computational tractability, and robustness. The recent publications reflect a strong trend in distributionally robust learning , offline reinforcement learning , and multi-agent systems , particularly in settings with delayed feedback, adversarial environments, and adaptive data collection. His articles span top journals in operations research, machine learning, and control theory. Scientific Awards and Honors: IBM Goldstine Fellowship (2019–2020) INFORMS Nicholson Award Finalist (2017, 2018) NSF and ONR grants (multiple, 2021–2027) NYU Research Catalyst Prize (2023) Horizon Robotics, Bain, and JP Morgan faculty awards (2021) CRA Outstanding Undergraduate Researcher (2013) Zhou advises PhD students in operations management and has served on dissertation committees at Georgia Tech and Tsinghua University. He has received over $1.8 million in research funding from NSF, ONR, and industry partners. He is actively involved in editorial roles as Associate Editor for Management Science , Operations Research , and Mathematics of Operations Research , and as Area Chair for NeurIPS, ICML, and ICLR. He also mentors high school students through logic and cryptography programs at Stanford’s Pre-Collegiate Summer Institute.

Lionel TruquetView profile

Lecturer

• Time Series Analysis
• Statistics for Dependent Data
• Markov Chains
• +3 more

Lionel Truquet is a Lecturer-Researcher in Statistics and Director of Research at ENSAI (École Nationale de la Statistique et de l'Analyse de l'Information). His research focuses on advanced statistical methodologies, including time series analysis, Markov chains, and ecological data modeling. He has contributed to multivariate autoregressive binary models, compact space time series models, and nonstationary count processes. Research Interests: His work emphasizes statistical theory applied to dependent data, with a strong focus on ecological applications. Key areas include ergodic properties of Markov chains, mixing properties of time series, and modeling presence-absence data. His methods address challenges in high-dimensional and nonstationary environments. Publications: Recent work includes influential contributions such as the TJALLING C. KOOPMANS ECONOMETRIC THEORY PRIZE-winning paper on iterations of dependent random maps. His publications span top journals like Bernoulli, the Annals of Applied Probability, and the Journal of Time Series Analysis, reflecting his expertise in both theoretical and applied statistics. Teaching: He teaches advanced courses such as Asymptotic Statistics and Dependence at the M2 level, reflecting his commitment to training future statisticians in cutting-edge methodologies.

Rocio Lilen SeguraView profile

Assistant Professor

• Civil Engineering
• Infrastructure Resilience
• Climate Change Adaptation
• +5 more

Rocio Lilen Segura is an Assistant Professor in the Department of Civil, Environmental and Sustainable Engineering at Santa Clara University's School of Engineering. She holds a Ph.D. in Civil Engineering from Sherbrooke University (2019) and a B.Sc. in Civil Engineering from Del Comahue National University (2013). Dr. Segura specializes in infrastructure resilience against extreme events and climate change, focusing on probabilistic risk assessment of critical infrastructure systems like dams and levees. Her work bridges civil engineering with machine learning and climate justice, integrating built, natural, and social systems to address environmental challenges. 2023 Severo Ochoa Mobility Programme grant 2019-2022 MITACS Accelerate industrial postdoc scholarship 2019 Léonard de Vinci medal and Scholarship Her research spans seismic risk reduction, surrogate modeling, and uncertainty quantification in dam engineering, with over 10 publications in journals like Advances in Civil Engineering and Water Journal. She previously collaborated with Hydro-Quebec during her postdoctoral research.

Ahmed Bin ZamanView profile

Assistant Professor

• computational biology
• evolutionary computation
• artificial intelligence
• +1 more

Ahmed Bin Zaman serves as an Assistant Professor in the Department of Computer Science at George Mason University, where his research bridges computational methods with biological discovery. His academic profile emphasizes innovative approaches to protein structure prediction and optimization challenges. His educational foundation includes: PhD in Computer Science, George Mason University (2021) Master of Science in Computer Science, George Mason University (2020) Zaman's research program centers on computational biology, with specialized expertise in evolutionary computation and artificial intelligence applied to protein conformation analysis. He develops stochastic optimization frameworks to enhance protein structure prediction accuracy, focusing on conformational space mapping and decoy ensemble generation. His methodology integrates evolutionary algorithms with multi-objective optimization to navigate complex molecular landscapes, contributing significantly to template-free protein structure determination. His publication trajectory from 2017-2022 reveals distinct research phases: initial work in cybersecurity threat detection evolved into a concentrated focus on computational structural biology. Thirteen protein-related publications demonstrate consistent innovation in conformational sampling techniques, while maintaining methodological rigor through evolutionary computation and machine learning integration. Key contributions include conformation space mapping frameworks and adaptive stochastic optimization systems that address decoy diversity challenges. Professional development shows progression from industry experience at Technext Limited (as team leader/researcher) and lecturing at Metropolitan University to his current academic role. His teaching philosophy emphasizes cultivating independent problem-solving capabilities in students through cognitive tool development.

Petr VozkaView profile

Assistant Professor

• Complex Chemical Mixtures Analysis
• Two-Dimensional Gas Chromatography
• Microplastics Analysis
• +7 more

Dr. Petr Vozka is an Assistant Professor in the Department of Chemistry and Biochemistry at California State University, Los Angeles, where he leads the Complex Chemical Composition Analysis Lab (C³AL). His research focuses on the characterization of complex chemical mixtures using state-of-the-art techniques, including two-dimensional gas chromatography and high-resolution mass spectrometry, with applications in environmental science, plastic waste conversion, and forensic analysis. Dr. Vozka's research interests span multiple critical areas including the analysis of complex chemical mixtures, microplastics in the environment, conversion of plastic waste into alternative fuels, environmental impact of oil spills, and forensic fingerprint analysis. His work on microplastics has gained significant attention, with media coverage highlighting his findings that microplastics can penetrate blood vessels and have been found in the brain. His research on the Huntington Beach oil spill investigated the long-term effects of oil contamination on beaches used for recreation. Dr. Vozka has organized significant academic events including the Multidimensional GC: From Petroleum to Beyond symposium at ACS Fall 2025 and serves on the organizing committee for the Multidimensional Chromatography Workshop (MDCW). His laboratory, C³AL, is equipped with advanced instrumentation including GC-TOFMS, GC×GC-FID, and GC×GC-TOFMS systems, supported by partnerships with LECO Corporation and Anton Paar. Recipient of LECO Corporation's Pegasus® BT GC-MS instrument through a competitive selection process Principal Investigator for research funded by Naval Air Warfare Center Aircraft Division Collaborator with researchers from Purdue University, UCT Prague, Delft University of Technology, and California State Polytechnic University Dr. Vozka actively mentors undergraduate and graduate students, with numerous students receiving research awards including CSU COAST Undergraduate Student Research Grants, NSF REU placements, and Dean's List honors. His students regularly present research at national conferences including ACS meetings and the Multidimensional Chromatography Workshop. The C³AL lab provides hands-on experience with cutting-edge analytical instrumentation, preparing students for careers in analytical chemistry and related fields. As part of the LECO-C³AL Facility partnership, Dr. Vozka's lab serves as a training ground for students in comprehensive two-dimensional gas chromatography and mass spectrometry techniques. The facility aims to enhance knowledge and expand opportunities for students while equipping them with skills necessary to excel in graduate programs and analytical positions in industry and the military.

• Proteomics
• Bioinformatics
• Cancer Diagnostics
• +3 more

Young-Hee Lee is a Ph.D. candidate and Lecturer at the Technical University of Munich (TUM), affiliated with the TUM School of Engineering and Design and the Institute for Communications and Navigation. Her research focuses on proteomics, with emphasis on protein citrullination dynamics, phosphoproteomics in cancer diagnostics, and advanced mass spectrometry techniques. Her recent work includes the development of high-throughput proteomic workflows for ischemic stroke biomarker discovery and the application of deep learning to enhance citrullination identification. She contributes to methodological innovations in peptide extraction and single-cell proteomics sensitivity. Lee is part of the Chair of Communication and Navigation led by Prof. Christoph Günther, located at Theresienstraße 90, Munich. Her research bridges computational biology and biochemical analysis, with applications in cancer, neuroscience, and viral proteomics.

George O'MahonyView profile

Lecturer

• Cybersecurity
• Cyber Resilience
• Network Security
• +8 more

Dr. George O'Mahony serves as Head of Department of Computer Science at Munster Technological University (MTU) and is a CONNECT Associate Investigator with Research Ireland. He leads Ireland's Cyber Range infrastructure development and acts as WorldSkills Ireland Expert for Cybersecurity Skill 54, driving national cybersecurity initiatives through academia-industry collaboration. Education: B.E. in Electrical and Electronic Engineering, University College Cork (UCC) Ph.D. in Electrical and Electronic Engineering, University College Cork (UCC), 2021 His research pioneers cyber resilience frameworks for OT/IoT systems, zero-trust architectures, and machine learning applications in anomaly detection. He develops low-complexity security solutions for resource-constrained edge devices, with emphasis on wireless sensor networks, GPS applications, and penetration testing methodologies. His work bridges theoretical innovation with practical infrastructure implementation. Recent publications reveal accelerating focus on quantum-resistant cryptography, MQTT-ZT secure brokers, and unified cyber resilience models. His scholarly output consistently addresses interference detection in wireless networks while expanding into satellite communications security and AI-driven network customization, reflecting strategic adaptation to emerging cyber threats. Research Leadership: CONNECT: Associate Investigator advancing cyber security research NCF Cyber Shock: Co-Principal Investigator Cyber Explore: Principal Investigator at MTU Cyber Range: National infrastructure lead (mobile/cloud) Horizon Telemetry: Core research team member As STEM advocate and Cyber Futures Academy contributor, O'Mahony shapes cybersecurity education through WorldSkills Ireland engagement and industry-focused cyber range deployments that serve academic, governmental, and commercial sectors.

• Signal Processing
• Statistical Signal Analysis
• High-Dimensional Data Analysis
• +3 more

Pascal Vallet is an Associate Professor at University of Bordeaux, affiliated with IMS Bordeaux (Integration Laboratory from Material to System) where he conducts research in the Signal and Image Processing group and Spectral team. His work focuses on theoretical and applied aspects of statistical signal processing with emphasis on high-dimensional data analysis. Vallet's research interests center on statistical signal processing methodologies, particularly in high-dimensional settings. His work addresses fundamental problems in covariance matrix analysis, spectral coherence, and change detection within Gaussian models. He develops theoretical frameworks for analyzing complex signals with applications in areas such as SAR imaging and multivariate time series analysis. His approach combines rigorous mathematical statistics with practical signal processing applications, focusing on scenarios where dimensionality challenges traditional statistical methods. Analysis of Vallet's recent publications reveals a consistent research trajectory focused on high-dimensional statistical signal processing. His work primarily addresses covariance structure analysis in Gaussian low-rank models, with applications to SAR imaging and multivariate time series. The publications demonstrate increasing sophistication in handling high-dimensional asymptotic regimes where both sample size and dimensionality grow large. His research shows strong theoretical foundations combined with practical validation through synthetic and real-world data experiments, particularly in radar imaging applications. Vallet maintains active research collaborations with several colleagues including Rémi Beisson, Audrey Giremus, Guillaume Ginolhac, and Philippe Loubaton, as evidenced by his co-authored publications across multiple years. His research contributes to advancing theoretical understanding of statistical signal processing in high-dimensional settings while maintaining relevance to practical applications in remote sensing and signal analysis.

Anna KorbaView profile

Assistant Professor

• Machine Learning
• Nonparametric Statistics
• Kernel Methods
• +5 more

Anna Korba is an Assistant Professor at École Polytechnique, specifically affiliated with ENSAE/CREST in the Statistics Department since September 2020. She is also a co-administrator of the Master Data Science program at École Polytechnique. Her academic journey has positioned her as a leading researcher in machine learning, with particular expertise in kernel methods, optimal transport, and statistical optimization. Dr. Korba received her PhD from Telecom ParisTech in 2018 under the supervision of Prof. Stephan Clémençon. Prior to her current position, she was a postdoctoral researcher at University College London's Gatsby Computational Neuroscience Unit working with Arthur Gretton from December 2018 to August 2020. Her academic foundation includes a Master's degree in Machine Learning and Computer Vision (MVA) from ENS Cachan and ENSAE in 2015. Anna Korba's research primarily focuses on machine learning with emphasis on kernel methods, optimal transport, optimization, particle systems, and preference learning. Her work bridges theoretical statistics with practical machine learning applications, particularly in developing novel sampling and optimization methods. She has made significant contributions to understanding Wasserstein gradient flows, density ratio estimation, and variational inference techniques. Her publication record demonstrates a strong trajectory in top-tier machine learning conferences including ICML, NeurIPS, AISTATS, and ICLR. Her research shows a clear evolution from foundational work on ranking and preference learning during her PhD to more recent contributions in Wasserstein-based optimization, sampling methods, and deep probabilistic modeling. The interdisciplinary nature of her work connects statistics, optimization theory, and practical machine learning applications. Top 10% Oral Presentation at AISTATS 2022 Top 15% Long Oral Presentation at ICML 2021 She actively mentors PhD students and postdoctoral researchers, currently advising seven PhD candidates and having successfully guided several alumni to prestigious positions. Dr. Korba also contributes to the academic community through her role in administering the Master Data Science program and collaborating with researchers across institutions worldwide. As part of the CREST research center, Dr. Korba works within a vibrant team of researchers focused on statistics, machine learning, and their applications to economic and social sciences. Her research group includes current PhD students and postdocs working on various aspects of her research interests, creating a dynamic environment for advancing the field of statistical machine learning.

• Sequential Decision Making
• Bandit Problems
• Theoretical Reinforcement Learning
• +6 more

Claire Vernade is a Group Leader at the University of Tübingen in the Cluster of Excellence Machine Learning for Science. She leads an active research group focused on theoretical aspects of sequential decision making, with particular expertise in bandit problems and reinforcement learning theory. Her work bridges theoretical foundations with practical applications in scientific discovery. Her research interests span sequential decision making, bandit problems, theoretical Reinforcement Learning, Learning Theory, and principled learning algorithms. She has made significant contributions to understanding non-stationary environments, lifelong learning frameworks, and the theoretical foundations of bandit algorithms. Her work on "Eigengame: PCA as a Nash Equilibrium" received an Outstanding Paper Award at ICLR 2021. Dr. Vernade has been awarded prestigious grants including an Emmy Noether award (2022) for her FoLiReL project and an ERC Starting Grant (2024) for her ConSequentIAL project. Her current ERC project explores the role of Reinforcement Learning in developing Continual Learning agents, with applications to scientific domains like drug discovery and micro-chemistry. Emmy Noether award under the AI Initiative call (2022) ERC Starting Grant (2024) Outstanding Paper Award at ICLR 2021 She currently supervises three PhD students and actively recruits postdocs and PhD candidates through the IMPRS-IS and ELLIS doctoral programs. Her group collaborates extensively with the broader machine learning community, organizing workshops like FoRLaC at ICML 2024 and serving as co-chairs for tutorials at major conferences. Dr. Vernade is also deeply committed to diversity and inclusion in machine learning, co-leading initiatives like Women in Learning Theory and Tübingen Women in Machine Learning.

Li SongView profile

Assistant Professor

• Computational Immunology
• Bioinformatics
• Genomics
• +3 more

Li Song is an Assistant Professor at the Department of Biomedical Data Science within the Geisel School of Medicine at Dartmouth College . He earned his Ph.D. in Computer Science (2018) and a Master's in Applied Mathematics and Statistics from Johns Hopkins University , advised by Liliana Florea. He completed postdoctoral training in the labs of X. Shirley Liu and Heng Li at the Data Science Department of Dana-Farber Cancer Institute . Education Ph.D. in Computer Science (Johns Hopkins University, 2018) M.S. in Applied Mathematics and Statistics (Johns Hopkins University) Research Focus : Designing algorithms for next-generation sequencing data analysis in immunology and microbiology. Key projects include: TRUST4 - De novo assembly of T-cell/B-cell receptors from RNA-seq T1K - Genotyping of polymorphic HLA/KIR genes Centrifuger - Compressed microbial genome classification Chromap - Ultrafast chromatin profiling data processing Recent Publications demonstrate expertise in computational immunogenetics, metagenomic analysis, and chromatin profiling workflows. The 2024 Genome Biology paper on Centrifuger received a Best Paper Award at RECOMB2024 . Training & Recruitment : The Song Lab actively seeks postdocs and graduate students through the Quantitative Biomedical Sciences program and Department of Computer Science at Dartmouth.

Ramana V DavuluriView profile

Professor

• Machine Learning
• Cancer Data Science
• Gene Regulation
• +4 more

Ramana V Davuluri serves as Professor in the Department of Biomedical Informatics at Stony Brook University's Renaissance School of Medicine. With over 20 years of experience in bioinformatics and computational genomics, he leads research at the intersection of machine learning and cancer genomics, focusing on translating high-dimensional -omic data into clinically actionable insights through statistically rigorous methodologies. Dr. Davuluri's research spans Machine Learning applications in Cancer Data Science , isoform-level gene regulation , and precision-medicine development. His lab pioneers bioinformatics solutions for genomic data interpretation, with emphasis on developing machine learning algorithms that convert NextGen sequencing outputs into experimentally testable discovery models. A core focus involves creating rapid biomarker identification systems from human tissue and blood samples through integrated computational-experimental approaches in systems biology. Analysis of his 2023-2025 publications reveals a dominant trend toward genomic foundation models (e.g., DNABERT variants), multi-omic cancer subtyping , and time-dependent therapeutic strategies for pediatric brain tumors and ovarian cancer. His work consistently bridges computational innovation with biological validation across diverse cancer types including glioma, lung adenocarcinoma, and high-grade serous carcinoma. As Principal Investigator for multiple multi-investigator and multi-site projects, Dr. Davuluri directs research integrating high-throughput experimental procedures with advanced data-mining techniques. His laboratory maintains strong collaborations across oncology, neuroscience, and immunology domains while developing genomics-based decision support systems for clinical translation. The Davuluri Lab employs a systems biology framework to develop novel informatics tools for precision oncology, with particular emphasis on translating genomic discoveries into clinical applications through biomarker discovery and therapeutic strategy optimization.

Ahmed SaeedView profile

Assistant Professor

• Scalable Computer Networks
• Congestion Control
• Operating Systems
• +5 more

Ahmed Saeed is an Assistant Professor in the School of Computer Science at Georgia Institute of Technology, specializing in scalable computer networks and systems. His research spans congestion control, operating systems, LEO satellite networks, and formal methods, with a strong record of publications and active mentorship. Education: PhD in Computer Science, Georgia Institute of Technology (2019) Bachelor's in Computer and Systems Engineering, Alexandria University (2010) Postdoctoral Associate, MIT (with Prof. Mohammad Alizadeh) Research Interests: Ahmed's work focuses on the theory, design, and implementation of scalable networked systems. Key themes include: Congestion control algorithms for datacenter and WAN traffic Overload control mechanisms for microsecond-scale RPCs Performance debugging tools for datacenter applications LEO satellite network modeling and policy analysis Formal verification of network protocols and resource schedulers Recent Publications Trend: His 2024-2025 papers emphasize LEO satellite resilience and datacenter performance , with contributions to emergency failover modeling, latency debugging tools, and congestion control protocols. These works combine empirical measurement, formal modeling, and policy recommendations. Awards & Funding: NSF CAREER Award (2024) – LEO satellite variability ($600k) NSF CNS Core Awards (2022) – Edge server stacks & formal verification (total $2.38M) Google Research Award (2022) – Scalable edge systems ($80k) DARPA Risers Top 5 Poster (2022) Spec Tech Award (2023) – Nanomodular electronics routing ($40k) Teaching & Service: He regularly teaches Computer Networking I (CS 3251) and Datacenter Networks & Systems (CS 8803) . Service includes PC roles for SIGCOMM, NSDI, CoNEXT, and Networking area co-chair for JSys. Lab & Students: Ahmed leads an active research group with PhD students Peidi Song, Bhaskar Pardeshi, Sherif Abdelrazek; MS students Dhyey Thummar, Pratyush Sahu, Sammy Kapoor; and undergraduate Demi Lei. Alumni have joined industry leaders like Juniper, Microsoft, and Snowflake.

Didier MeuwlyView profile

Professor

• Forensic Science
• Biometrics
• Probabilistic Evaluation
• +3 more

Didier Meuwly is a Full Professor of Forensic Biometrics at the University of Twente (since 2013) and Principal Scientist at the Netherlands Forensic Institute (NFI). His work focuses on automating and validating probabilistic evaluation of forensic evidence, particularly biometric traces. He has contributed to international standards via ISO Technical Committee 272 and served as Associate Editor for Forensic Science International . PhD in Forensic Speaker Recognition (University of Lausanne, 2000) Research spans forensic biometrics, likelihood ratios, AI validation, and gait/body analysis from surveillance footage. Recent work addresses ISO standards (21043), forensic AI explainability, and multimodal evidence evaluation. His publications emphasize empirical validation and statistical rigor. Key awards include: ENFSI Distinguished Forensic Scientist Award (2022) University of Lausanne Law Faculty Prize (2002) Active in global forensic networks, he chairs the ENFSI R&D Committee and collaborates across disciplines on digital evidence, biometric security, and forensic methodology.

Mahmoud MoradiView profile

Professor

• Biomolecular Simulations
• Molecular Dynamics
• Protein Conformational Dynamics
• +5 more

Mahmoud Moradi is a Professor of Physical Chemistry at the University of Arkansas, holding the distinguished position of Bruker Analytical Science Professor in the Department of Chemistry & Biochemistry within the College of Arts & Sciences. With a strong background in physics and computational chemistry, he leads the Biomolecular Simulations Lab (BioSimLab) focusing on protein dynamics and membrane transport mechanisms. Education: Ph.D. in Physics from North Carolina State University M.Sc. in Physics from Sharif University of Technology, Iran B.Sc. in Physics from Sharif University of Technology, Iran Postdoctoral Associate at Beckman Institute and Department of Biochemistry, University of Illinois at Urbana-Champaign (2011-2015) Professor Moradi's research centers around two inter-related questions: (i) how do proteins function by changing their conformation and undergoing concerted motions? and (ii) how can we simulate these functionally important conformational changes at an atomic level? His lab develops and employs Molecular Dynamics (MD) based enhanced sampling techniques to tackle both problems. They are particularly interested in the study of large-scale conformational changes of proteins such as those involved in membrane transport and signal transduction. A novel combination of nonequilibrium and equilibrium MD based techniques are employed to reconstruct the slow biomolecular processes such as inward- to outward-facing structural transition of membrane transporters at an atomic level. This research has implications for understanding disease mechanisms at a molecular level and improving computational methodologies for biomedically relevant applications. Professor Moradi's recent publications demonstrate a strong focus on membrane protein dynamics, particularly examining cholesterol-protein interactions, GPCR mechanisms, and membrane transporter functions. His work increasingly integrates experimental and computational approaches, with a notable emphasis on SARS-CoV-2 spike protein dynamics during the pandemic years. The publications consistently apply advanced molecular dynamics techniques to investigate protein conformational changes, with growing applications in drug delivery systems and computational drug design. Scientific Awards: NIH MIRA (R35) Award, 2022-2027 (as PI): Physics-based characterization of functionally relevant protein conformational dynamics NSF I-Corps Award, 2021-2022 (as PI): Physics-Based Binding Affinity Estimator NSF CAREER Award, 2020-2025 (as PI): Riemannian Reformulation of Collective Variable Based Free Energy Calculation Methods NIH R15 Grant, 2020-2023 (as PI): Molecular Characterization of the Influenza Hemagglutinin Mediated Membrane Fusion DOE Grant, 2020-2023 (as co-PI): Protein Targeting to the Chloroplast Thylakoid Membrane NSF HDR Grant, 2019-2022 (as PI): Atomic Level Structural Dynamics in Catalysts Connor Endowed Faculty Fellowship, 2018 Fulbright College Outstanding Researcher Award for 2023/24 academic year Professor Moradi has successfully mentored numerous graduate students through their PhD programs, with recent graduates including Ehsaneh Khodadadi, Shadi Badiee, and Ugochi Isu. His research is supported by substantial grants from NIH, NSF, and DOE, totaling millions of dollars in funding. His NIH MIRA award represents a significant long-term commitment to his research program, while his NSF CAREER award recognizes both research excellence and educational contributions. The Biomolecular Simulations Lab at the University of Arkansas is a vibrant research group that combines computational and experimental approaches to study protein dynamics, maintaining strong collaborations with experimental groups both within the university and at other institutions.