جست‌وجوی استاد و پژوهشگر

John G. Kernsمشاهده پروفایل

استاد

• Translational Clinical Neuroscience
• Psychotic Disorders
• Cognitive and Emotional Control
• +۵ مورد دیگر

John G. Kerns is a Professor in the Department of Psychology at the University of Missouri, leading the Cognitive and Emotional Control Lab. His research integrates clinical psychology and neuroscience to understand psychotic disorders, with a focus on striatal functioning and aberrant salience. His primary research interests include: Translational clinical neuroscience of psychotic disorders Cognitive and emotional control mechanisms Striatal functioning and feedback-related learning Aberrant salience and positive schizotypy Openness to experience in psychopathology Analysis of Dr. Kerns' recent publications shows a strong emphasis on neuroimaging and behavioral studies of psychosis risk, particularly examining corticostriatal pathways and their relationship to cognitive deficits. His work consistently bridges personality factors with neural mechanisms. No scientific awards are mentioned in the provided text. Dr. Kerns mentors a vibrant team of graduate and undergraduate students. Current graduate students include Megan Liew and Tyler Rogers, and his alumni have secured prestigious positions in academia and clinical practice. He is accepting applications for graduate students starting in Fall 2026. The Cognitive and Emotional Control Lab provides a collaborative environment for research on psychotic disorders, utilizing behavioral, clinical, and neuroimaging methods to advance understanding and treatment.

Andrew J Margenotمشاهده پروفایل

دانشیار

• Soil Science
• Environmental Science
• Agricultural Chemistry
• +۶ مورد دیگر

Andrew J Margenot is an Associate Professor in the Department of Crop Sciences at the University of Illinois Urbana-Champaign, with affiliations at the Institute for Sustainability, Energy, and Environment, Center for Digital Agriculture, and National Center for Supercomputing Applications (NCSA). His research focuses on soil biogeochemistry, particularly phosphorus and carbon cycling, soil health, and agroecosystem management. He explores topics such as soil enzyme activity, nutrient loss mitigation, and the impacts of agricultural practices on environmental systems. Margenot leads exploration into organic matter recycling, legacy phosphorus dynamics, and the application of advanced analytical techniques like radioisotopic labeling and spatial modeling. His work bridges field experimentation with computational methods to address global challenges in sustainable agriculture and environmental stewardship. Research Interests: Soil phosphorus and carbon biogeochemistry Soil health indicators and enzyme activity Agricultural nutrient management and loss mitigation Long-term agricultural experiment analysis (e.g., Morrow Plots) Impacts of land-use change on soil properties Integration of digital technologies in agricultural research Recent Articles Trends: Margenot’s recent work emphasizes methodological advancements in soil analysis (e.g., enzyme assays), phosphorus cycling dynamics in diverse ecosystems, and agricultural sustainability. Key themes include evaluating fertilizer forms, optimizing nutrient use efficiency, and quantifying environmental impacts of farming practices. Labs & Collaborations: He collaborates across institutions on projects involving soil biogeochemistry, computational modeling (via NCSA), and interdisciplinary sustainability initiatives. His research often involves field experiments, isotopic tracing, and multi-institutional datasets.

Mark Kramerمشاهده پروفایل

استاد

• Neuroscience
• Computational Neuroscience
• Mathematical Modeling of Neural Activity
• +۵ مورد دیگر

Mark Kramer is a Professor in the Department of Mathematics & Statistics at Boston University. He belongs to the Applied Mathematics research group, focusing on mathematical, statistical, and machine learning approaches to characterize brain activity. His work bridges data-driven neuroscience with computational methods, exploring topics like biophysical models of neurons, field models of neural populations in epilepsy, and theoretical questions about brain rhythms. His research interests include: Biophysical modeling of single-neuron dynamics Neural population activity in pathological states Machine learning for detecting abnormal brain rhythms Analysis of cross-frequency coupling and coherence Kramer has developed educational resources like Case Studies in Neural Data Analysis using both MATLAB and Python. These materials teach practical data analysis techniques for spike trains and field data, emphasizing hands-on implementation over theoretical mathematics. He has received funding from NIH and NSF for computational neuroscience projects. His recent publications focus on epilepsy research, sleep spindle analysis, and neural signal processing. The work spans from developing statistical frameworks to understanding network dynamics in seizure termination and exploring phase consistency in neural data. Notably, his coherence studies revealed non-intuitive coupling patterns between brain regions, demonstrating that low-amplitude rhythms can be more informative than dominant ones.

Zhang Chuanzengمشاهده پروفایل

استاد

• Acoustic and elastic wave propagation
• Mechanics of smart materials and structures
• Phononic crystals and wave control
• +۷ مورد دیگر

Professor Chuanzeng Zhang is a renowned academic currently serving as Professor (C4) and Chair of Structural Mechanics at the Department of Civil Engineering, University of Siegen, Germany. He has held leadership roles including Head of the Department of Civil Engineering (2015–present) and Director of the Institute of Structural Engineering (2007–2010). With a habilitation from Technical University Darmstadt and a PhD from Northwestern University, his research spans acoustic wave propagation, computational mechanics, and multifunctional materials. Education : PhD (Dr.-Ing.) from Northwestern University (USA), Habilitation from Technical University Darmstadt (Germany), Diploma (Dipl.-Ing.) from Technical University Darmstadt. Key Roles : Guest Professor at multiple Chinese institutions (Harbin Institute of Technology, Tongji University), Vice-President of the International Chinese Association for Computational Mechanics. Research interests include: Mechanics of smart materials and structures Wave propagation in phononic crystals Fracture mechanics in composite materials Computational methods for anisotropic solids Surface and interface effects in nanomaterials Multifunctional and functionally graded materials Recent Publications : His work focuses on advanced computational methods for wave propagation, fracture analysis, and smart materials, with applications in phononic crystals, piezoelectric composites, and functionally graded structures. Honors & Awards : 2015: Du Qinghua Medal for Computational Methods 2015: Member of European Academy of Sciences and Arts 2015: Honorary Doctorate from Slovak University of Technology 2014: Member of European Academy of Sciences Leadership & Collaborations : Led Senate Commissions, Research Advisory Committees, and DVM Working Groups. Maintains strong academic ties with institutions in China and Germany.

• Compiler Optimization for High Performance Computing
• Optimization of Sparse/Dense Matrix/Tensor Computations
• Scalable Machine Learning
• +۱ مورد دیگر

P. (Saday) Sadayappan is a Professor in the School of Computing at the University of Utah. He serves as a lead researcher in high-performance computing, with a focus on compiler optimization and algorithm-architecture co-design. His current projects include NIH SBIR Phase 2 funding for large-scale image analysis and NSF grants for tensor applications and cyber-infrastructure for AI. Research Interests : Compiler Optimization for High Performance Computing Optimization of Sparse/Dense Matrix/Tensor Computations Scalable Machine Learning Algorithm-Architecture Co-Design Optimization Research Trends in Publications : His work emphasizes optimizing computational workflows for emerging hardware architectures, with a focus on accelerating machine learning and scientific computing through compiler-level innovations. Recent trends include co-design for CNNs, sparse matrix optimizations, and distributed algorithms. Scientific Awards : ACM SIGPLAN Most Influential PLDI Paper Award (2018) Grants & Projects : NSF (2022–2027): Comprehensive Framework for Tensor Applications NSF AI Institute ICICLE (2021–2026): Cyber-infrastructure for environmental AI NIH SBIR (2023–2025): Next-gen machine learning for image analysis Labs & Teams : Collaborates with institutions like Ohio State University and RNET Technologies on projects involving parallel computing, sparse algorithms, and compiler design.

Claudia Bußمشاهده پروفایل

استاد

• Pre- and postnatal programming of health across the lifespan
• Brain development
• Maternal stress effects on fetal development
• +۷ مورد دیگر

Claudia Buß is a University Professor (W3) at the Institute of Medical Psychology, Charité – Universitätsmedizin Berlin, where she has been serving since 2019. She also holds an Adjunct Professor position in the Department of Pediatrics at the University of California Irvine School of Medicine since 2022, having previously served as Adjunct Associate Professor (2016-2022) and Adjunct Assistant Professor (2013-2016). Additionally, she is a faculty member at both the Berlin School of Mind & Brain and the Einstein Center for Neurosciences in Berlin. Dr. Buß received her psychology education at the University of Trier, Germany, and completed her PhD in Psychobiology at the University of Trier and McGill University in Montreal, Canada in 2006. She conducted postdoctoral research in the Department of Psychiatry and Human Behavior at the University of California Irvine, USA, before becoming an Assistant Professor in the Department of Pediatrics at the same institution from 2010-2012. Professor Buß's research focuses on the pre- and postnatal programming of health and illness across the lifespan, with particular emphasis on brain development. Her work investigates how maternal stress during pregnancy affects fetal development and disease vulnerability, including the intergenerational transmission of early childhood stress and trauma effects. She employs advanced imaging techniques such as MRI and EEG to examine stress effects on the brain and cognitive performance throughout life. Her extensive publication record demonstrates a consistent trajectory in understanding how maternal factors—including cortisol levels, inflammatory markers like IL-6, and childhood trauma history—influence fetal and newborn brain development. These studies frequently connect prenatal exposures to specific brain structures (amygdala, hippocampus, hypothalamus) and subsequent behavioral outcomes in children. Her recent work has expanded to examine multiple simultaneous exposures ("poly-exposures") and their cumulative effects on neurodevelopment. 2017: Elected Member, Academy of Behavioral Medicine Research (ABMR) 2015: Curt Richter Award of the International Society for Psychoneuroendocrinology (ISPNE) for "distinguished early to mid-career scientific contributions at the interface of health-related biological and psychological processes" As a principal investigator, Professor Buß leads research examining the developmental origins of health and disease, with particular focus on how early life experiences shape brain development and long-term health outcomes. Her work bridges basic science and clinical applications, investigating both biological mechanisms (endocrine, inflammatory) and psychological factors that influence the maternal-fetal interface. She collaborates extensively across institutions, as evidenced by her dual appointments in Berlin and California, and her involvement with the Berlin School of Mind & Brain and Einstein Center for Neurosciences. Professor Buß's laboratory utilizes a multidisciplinary approach combining psychological assessment, biological sampling, and advanced neuroimaging to investigate how maternal experiences become biologically embedded and transmitted across generations. Her team employs ecological momentary assessment methods to capture real-time stress experiences during pregnancy, providing a more accurate picture of maternal exposures than retrospective reports.

Ming Yuanمشاهده پروفایل

استاد

• High-dimensional statistics
• Machine learning
• Tensor methods
• +۴ مورد دیگر

Ming Yuan is a Professor in the Department of Statistics at Columbia University and serves as Associate Director of the Data Science Institute. His research focuses on high-dimensional statistics, machine learning, and statistical methodology with applications in genomics, finance, and imaging. Yuan holds a Ph.D. in Statistics from the University of Wisconsin-Madison (2004) and a B.S. in Electrical Engineering from the University of Science and Technology of China (1997). Education: 2004 Ph.D., Statistics, University of Wisconsin-Madison 2003 M.S., Computer Science, University of Wisconsin-Madison 2000 M.S., Probability and Statistics, University of Science and Technology of China 1997 B.S., Electrical Engineering, University of Science and Technology of China Research Interests: Dr. Yuan’s work bridges theoretical and applied statistics, emphasizing scalable methods for high-dimensional data. Key areas include tensor decomposition, covariance estimation, and statistical machine learning. His contributions to methods like sparse inverse covariance estimation and matrix/tensor completion have found applications in finance, genomics, and image analysis. Publications: His recent work explores tensor-based methods for high-dimensional analysis and develops optimal algorithms for compressed sensing. Articles often address statistical theory and computational challenges in modern data science, reflecting a balance between foundational and applied research. Awards: 2025 JASA Theory & Method Invited Discussion Paper 2024 William F. Sharpe Award (JFQA) 2018 Medallion Lecturer (Institute of Mathematical Statistics) 2014 Guy Medal in Bronze (Royal Statistical Society) 2007 Leo Breiman Junior Award Professional Activities: Yuan has served as Co-Editor of The Annals of Statistics (2019–2021) and Program Secretary for the Institute of Mathematical Statistics (2018–2021). His work integrates interdisciplinary collaborations, particularly in biomedical imaging and financial econometrics.

Gloria Robertsمشاهده پروفایل

پژوهشگر ارشد

• Neuroscience
• Psychiatry
• Bipolar Disorder
• +۳ مورد دیگر

Dr. Gloria Roberts is a Research Fellow at the Black Dog Institute, affiliated with the University of New South Wales' Faculty of Medicine, School of Psychiatry. Her research focuses on identifying predictors of bipolar disorder development in high-risk populations, with particular emphasis on neural mechanisms of executive functioning and emotional processing. Location: Black Dog Institute, Hospital Road, Prince of Wales Hospital, Randwick NSW 2031 Contact: +61 2 9382 8324 | ORCID: https://orcid.org/0000-0002-1966-5120 Education Background: B.Sc in Applied Psychology (University College Cork, Ireland, 2002) M.Sc in Neuropharmacology (National University of Ireland Galway, Ireland, 2003) Diploma in Statistics (Trinity College Dublin, Ireland, 2006) PhD in Neuroscience (Trinity College Dublin, Ireland, 2008) Dr. Roberts' research program centers on the neural basis of emotional dysregulation characteristic of mood disorders, employing structural and functional Magnetic Resonance Imaging as her primary research tool. Her work integrates advanced neuroimaging analysis techniques including diffusion tensor imaging tractography, dynamic causal modeling, graph theory, and machine learning approaches. She maintains active collaborations with Queensland Institute of Medical Research (Brisbane), Neuroscience Research Australia (Sydney), and the Centre for Healthy Brain Ageing (Sydney). Analysis of Dr. Roberts' publication record (94 journal articles, 2 book chapters, 25 conference papers) reveals a consistent research trajectory focused on neurocognitive patterns in bipolar disorder. Her recent work increasingly incorporates machine learning techniques to identify predictive biomarkers, with a growing emphasis on longitudinal studies tracking high-risk populations. The interdisciplinary nature of her research bridges neuroscience, psychiatry, and computational methods to address fundamental questions about mood disorder development. Scientific Contributions: Extensive publication record across multiple formats (journal articles, book chapters, conference presentations) Development of innovative neuroimaging analysis techniques for bipolar disorder research Establishment of multi-institutional collaborations across Australia Integration of machine learning approaches with traditional neuroimaging methods Dr. Roberts actively mentors junior researchers and contributes to the broader scientific community through peer review activities and participation in research networks focused on mood disorders. Her work has significant implications for early intervention strategies and the development of novel therapeutic approaches for bipolar disorder.

• High-dimensional probability
• Randomized algorithms
• Numerical linear algebra
• +۴ مورد دیگر

Elizaveta Rebrova is an Assistant Professor in the Department of Operations Research and Financial Engineering (ORFE) at Princeton University. She is also associated with the Program in Applied and Computational Mathematics (PACM) and the Center for Statistics and Machine Learning (CSML). Education: Specialist degree from Moscow State University (2012), PhD in Mathematics from University of Michigan (2018 under Roman Vershynin) Prior Appointments: Postdoctoral Scholar at Lawrence Berkeley National Lab (2021), Assistant Adjunct Professor at UCLA Mathematics Department (2018-2021) Her research focuses on randomized numerical linear algebra , mathematics of data science , and high-dimensional probability . Key interests include developing algorithms for large-scale data with non-trivial structure, robust and interpretable learning, and stochastic optimization. Her recent work analyzes algorithmic convergence in structured settings, tensor-based data compression, and nonnegative matrix/tensor factorization under constraints. Recent publications span topics in randomized NLA , robust solvers , tensor methods , and nonnegative matrix factorization . Notable trends include improving convergence rates for iterative methods, handling adversarial noise in linear systems, and leveraging tensor structures for efficient data recovery. She supervises Ph.D. students including Jackie Lok, Shambhavi Suryanarayanan, and Sofiia Shvaiko. Her teaching at Princeton covers graduate probability theory (ORF526), convex optimization (ORF523), and network science (ORF387), with prior teaching roles at UCLA and University of Michigan.

• Substance Use Disorders
• Alcohol Use Disorders
• Tobacco Use Disorders
• +۵ مورد دیگر

Walter Roberts is an Assistant Professor in the Psychiatry Department at Yale School of Medicine with a secondary appointment in Biomedical Informatics & Data Science. A licensed clinical psychologist specializing in substance use treatment, particularly alcohol and tobacco use disorders, he combines clinical expertise with technological innovation in his research program. Dr. Roberts received his PhD in Clinical Psychology from the University of Kentucky in 2016. His research program employs multiple methodologies to identify the causes and consequences of substance use in humans, with a focus on developing mobile biosensor platforms that can passively detect and predict substance use behaviors. These systems, built using machine learning and informatics techniques, aim to improve assessment and targeted delivery of interventions in clinical and research settings. He also investigates risk factors for hazardous alcohol use using clinical and epidemiological data and maintains an active interest in research ethics related to substance use research. Dr. Roberts' publication record reveals a strong focus on alcohol use disorders, with increasing integration of digital phenotyping and AI technologies in recent years. His work spans from traditional clinical studies to cutting-edge investigations using wearable technology and machine learning. The research shows a clear trajectory toward developing objective, real-time assessment methods for substance use behaviors, with particular attention to sex and gender differences in alcohol-related outcomes. Recent publications demonstrate growing interest in ethical considerations surrounding digital technologies in substance use research. MD2K Training Institute Summer Scholar (2023) Early Career Scientist Award from CHADD (2015) Enoch Gordis Award Finalist from Research Society on Alcoholism (2013) Dr. Roberts serves as a Sub Investigator on multiple clinical trials related to alcohol addiction and mental health, including studies examining the effects of acute stress and inflammation on drinking behavior, combining varenicline and guanfacine for smoking cessation, and investigating whether propranolol attenuates stress-induced drinking. His collaborative network includes frequent co-authors such as Sherry McKee, Terril Luce, MacKenzie Peltier, Yasmin Zakiniaeiz, and Bubu Banini. His research has been featured in YaleNews, highlighting work on using smartwatches to better understand psychiatric illness.

Vijayaraghavan Aravindanمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Machine Learning
• Quantum Information
• +۳ مورد دیگر

Dr. Vijayaraghavan Aravindan is an Associate Professor in the Department of Computer Science at Northwestern University, with a courtesy appointment in Industrial Engineering & Management Sciences. He is a core member of the Theory CS Group and serves as the Site Director for the NSF-funded Institute for Data, Economics, Algorithms, and Learning (IDEAL). His research focuses on theoretical computer science, particularly algorithmic foundations of machine learning, quantum information, and beyond worst-case analysis. Education: Ph.D. and M.A. in Computer Science, Princeton University B.Tech. in Computer Science and Engineering, Indian Institute of Technology Madras Research Interests: His work bridges theoretical computer science and machine learning, emphasizing efficient algorithms for high-dimensional data, quantum entanglement certification, and adversarial robustness. He explores paradigms such as smoothed analysis and stability-based approaches to provide practical algorithmic guarantees. Awards & Grants: NSF CAREER Award Google Research Scholar NSF AITF Grants (CCF-1637585, CCF-2154100) Advising & Teaching: He advises PhD students on topics like quantum computing, robust machine learning, and optimization. Courses taught include graduate algorithms, theoretical foundations of data science, and quantum computation. His lab collaborates on projects at IDEAL and with institutions like Carnegie Mellon University and TTI Chicago. Professional Leadership: Served as FOCS 2024 General Chair and organizes the McCormick Theory Workshops. Active on program committees for ICML, COLT, and NeurIPS.

Erik Quaeghebeurمشاهده پروفایل

استادیار

• Probability Theory
• Statistics
• Uncertainty in Artificial Intelligence
• +۴ مورد دیگر

Erik Quaeghebeur is an Assistant Professor at Eindhoven University of Technology's School of Mathematics and Computer Science, focusing on uncertainty modeling in artificial intelligence. His work spans probabilistic circuits, imprecise probability theory, and wind energy applications. PhD in Applied Mathematics (Ghent University, 2002-2009) Master's in Applied Mathematics (Université catholique de Louvain, 2001-2002) Master's in Physics Engineering (Ghent University, 1998-2001) Research interests include probabilistic modeling under uncertainty, with applications in AI and wind energy systems. His recent work explores tensor factorizations, equivariant graph neural networks, and scalable probabilistic circuits. Scientific contributions include 60 research outputs and 2 datasets . Awards encompass the ERCIM Alain Bensoussan Fellowship (2013), BOF Postdoc (2010), and B.A.E.F. Francqui Fellowship (2009). He serves on committees for the Society for Imprecise Probability and acts as editorial board member for related conferences. Foundations of Artificial Intelligence course (since 2020) Uncertainty Representations and Reasoning course (since 2021)

Eric F. Lockمشاهده پروفایل

دانشیار

• Statistical genomics
• High-dimensional data analysis
• Machine learning
• +۷ مورد دیگر

Eric F. Lock is an Associate Professor in the Division of Biostatistics & Health Data Science at the University of Minnesota's School of Public Health. He is also a Member of the Masonic Cancer Center (MCC) and has been at the University of Minnesota since 2014, after completing his PhD in Statistics from the University of North Carolina in 2012 and a postdoctoral fellowship in Statistical Genomics at Duke University in 2014. Lock's research focuses on developing methods for the analysis of multi-faceted high-dimensional data, particularly in "omics" fields such as genomics, metabolomics, and proteomics. His work emphasizes the integrated analysis of data from multiple sources (e.g., gene expression, metabolomics, imaging) or measured in multiple dimensions (e.g., multiple tissue types or body regions). He also specializes in exploratory factorization and clustering methods, and Bayesian nonparametric inference. His recent publications demonstrate significant contributions to tensor data imputation (BAMITA), matrix decomposition (EV-BIDIFAC), and methods for handling complex genomic data. His work bridges statistical theory with practical applications in molecular biology, addressing challenges in data integration across multiple biological measurement platforms. Delta Omega, Honorary Society in Public Health (2019) As an active researcher and educator, Lock serves on dissertation committees, including for Mykhaylo M. Malakhov who recently defended his PhD at the University of Minnesota School of Public Health. He is involved in organizing and participating in major conferences such as STATGEN 2025, demonstrating his leadership in the biostatistics community.

David Shermanمشاهده پروفایل

دانشیار

• Functional Analysis
• Operator Algebras
• Noncommutative Lp Spaces
• +۴ مورد دیگر

David Sherman is an Associate Professor in the Department of Mathematics at the University of Virginia, part of the College of Arts & Sciences. His research focuses on functional analysis and operator algebras, with specialized interests in noncommutative L p spaces, operator theory within von Neumann algebras, model theory of operator algebras, and noncommutative convexity. He has contributed to foundational studies of noncommutative L p spaces, isometries between operator algebras, and applications of logic to operator algebras. Recent teaching includes advanced calculus, linear algebra, and operator theory courses. His work bridges pure mathematics with interdisciplinary themes, such as applying set-theoretic methods to algebraic structures and exploring connections between operator algebras and logic. Sherman has co-authored significant papers on topics like support expansion C*-algebras and quantization of coarse spaces, reflecting his expertise in modern operator theory. His research outputs emphasize structural properties of operator algebras, with notable contributions to the classification of II₁ factors and model-theoretic approaches to noncommutative systems. While no specific awards are listed, his extensive publication record and teaching roles highlight his academic impact. Sherman maintains administrative involvement in undergraduate mathematics programs and graduate initiatives at UVA.

Manohar N. Murthiمشاهده پروفایل

دانشیار

• Machine Learning
• Quantum Computing
• Signal Processing
• +۵ مورد دیگر

Manohar N. Murthi serves as an Associate Professor in the Department of Electrical & Computer Engineering at the University of Miami's College of Engineering. His academic profile shows active engagement across both technical engineering domains and social science research, with recent publications spanning quantum computing applications, neural network models for biomedical signal processing, and political conspiracy theories. Dr. Murthi's research interests bridge multiple disciplines, with primary focus areas including machine learning, quantum computing, signal processing, belief theory, and conspiracy theory research. His work demonstrates a unique interdisciplinary approach that connects electrical engineering methodologies with social science applications, particularly in analyzing belief systems and misinformation patterns. The breadth of his research is evident in publications ranging from technical algorithms for Dempster-Shafer belief theory to sociological studies on White Replacement theory and QAnon conspiracy beliefs. Analysis of his recent publications (2020-2024) reveals two distinct but complementary research trajectories: technical work in quantum tensor networks, graph neural networks, and belief theory frameworks; and social science applications examining conspiracy theories, political extremism, and misinformation. His technical papers often develop novel computational frameworks for uncertainty quantification and data analysis, while his social science work applies these methodologies to understand belief formation and political behavior. This dual focus creates a distinctive research profile that connects engineering rigor with social science insights. Dr. Murthi maintains active research collaborations, particularly with Kamal Premaratne, across multiple publications in both engineering and political science journals. His work has been published in venues including IEEE transactions, The Journal of Politics, and Politics, Groups and Identities, demonstrating successful cross-disciplinary scholarship. His research appears to be supported by collaborative grants, though specific funding sources aren't detailed in the available information. While specific laboratory information isn't provided in the source material, Dr. Murthi's research suggests involvement in computational laboratories focused on machine learning, signal processing, and data analysis. His work with sEMG signals for gesture recognition indicates potential connections to biomedical engineering labs, while his belief theory research suggests computational theory groups. His interdisciplinary approach likely involves collaboration across multiple research teams within and beyond the College of Engineering.