جست‌وجوی استاد و پژوهشگر

Simon Foucartمشاهده پروفایل

استاد

• Mathematical Data Science
• Compressive Sensing
• Approximation Theory
• +۵ مورد دیگر

Simon Foucart is a Professor in the Department of Mathematics at Texas A&M University, affiliated with the College of Arts & Sciences, and serves as Associate Director of the Institute of Data Science. He holds a PhD from the University of Cambridge (2005) and prior degrees from Ecole Centrale Paris and the University of Cambridge's Part III Math Tripos. His research focuses on Mathematical Data Science, Compressive Sensing, and Approximation Theory, with applications in Engineering and Bioinformatics. Key roles include Editorial Board memberships for major journals like Journal of Approximation Theory and Constructive Approximation . He has supervised numerous PhD students and postdocs, including current advisee Thomas Winckelman. Notable awards include the Presidential Impact Fellow (2019) and Heilbronn Distinguished Visiting Fellow (2024). His publications span optimal recovery, sparse recovery algorithms, neural network theory, and computational frameworks. Recent work emphasizes algorithmic solutions for data science challenges, including low-rank matrix recovery and graph signal processing. His software contributions include MATLAB packages like MinProj and Basc for approximation and optimization tasks. Teaching includes graduate courses on Mathematical Data Science and approximation theory, supported by his textbook Mathematical Pictures at a Data Science Exhibition . He has held visiting positions at institutions such as the Isaac Newton Institute and Los Alamos National Laboratory.

Eric Chiمشاهده پروفایل

دانشیار

• Statistical Learning
• Numerical Optimization
• Biological Science Applications
• +۳ مورد دیگر

Eric Chi is an Associate Professor in the Department of Statistics at Rice University, also affiliated with the Ken Kennedy Institute. He holds a B.A. in Physics from Rice University, an M.S. in Electrical Engineering from UC Berkeley, and a Ph.D. in Statistics from Rice University. His research focuses on statistical learning and numerical optimization, applied to large-scale data in biological and engineering domains. He has been recognized with awards including the NSF CAREER Award (2018) and the LeRoy and Elva Martin Teaching Excellence Award (2020). Chi's academic journey includes postdoctoral roles at UCLA and Rice, and prior faculty experience at North Carolina State University (2015-2021). He serves as an Associate Editor for the Journal of Computational and Graphical Statistics since 2016 and on the Editorial Board of Statistical Methods in Medical Research since 2011. His work emphasizes algorithmic development for complex data structures, including tensor decomposition, matrix completion, and convex clustering. Recent research trends include robust low-rank tensor decomposition, Bayesian inference via proximal MCMC, and convex-nonconvex regularization strategies. His publications span statistical computing, machine learning, and interdisciplinary applications. Chi advises current PhD students at Rice and has mentored multiple alumni now in academia and industry roles.

Erich Kaltofenمشاهده پروفایل

استاد

• Computational Algebra
• Symbolic Computation
• Number Theory
• +۴ مورد دیگر

Erich Kaltofen is a Professor of Mathematics and member of the Symbolic Computation group at North Carolina State University (NCSU). He holds an adjunct professorship in Computer Science at Duke University. His academic journey includes a PhD in Computer Science from Rensselaer Polytechnic Institute (1982) and an MS from the same institution (1979). Kaltofen’s research focuses on computational algebra, hybrid symbolic-numeric algorithms, sequential and parallel algorithms, and symbolic computation systems. Research Interests: Kaltofen’s work spans computational algebra and number theory, emphasizing the development of efficient algorithms for polynomial factorization, sparse interpolation, and error-correcting methods. He is renowned for contributions to symbolic computation systems like LinBox and FoxBox, and his research integrates theoretical insights with practical software development. Publications: His recent publications highlight advancements in sparse polynomial interpolation, error-correcting algorithms, and computational complexity. Key themes include symbolic-numeric computation, algorithm design, and applications in cryptography and algebraic geometry. Awards and Recognition: A Fellow of the ACM (2009) and recipient of the Jenks Memorial Prize (2017), Kaltofen’s contributions have shaped the field of symbolic computation. His work bridges theoretical foundations and real-world applications, impacting computer science and mathematics. Advising and Grants: Kaltofen has advised numerous PhD students and postdocs, fostering advancements in algorithmic research. His grants include NSF-funded projects on hybrid symbolic-numeric methods and error-correcting algorithms. He collaborates on initiatives such as the Genomic Sciences program at NCSU. Labs and Teams: Co-designer of the LinBox library, Kaltofen contributes to open-source software for linear algebra and symbolic computation. His work emphasizes collaborative, interdisciplinary projects addressing challenges in computational mathematics and computer science.

Konstantinos Daniilidisمشاهده پروفایل

استاد

• Computer Vision
• Robotics
• 3D Perception
• +۷ مورد دیگر

Konstantinos Daniilidis is the Ruth Yalom Stone Professor at the Department of Computer and Information Science, University of Pennsylvania, within the School of Engineering and Applied Science. He holds an adjunct position at Archimedes, Athena Research Center, Greece. His research focuses on computer vision, robotics, and geometric methods in AI, with a strong emphasis on 3D perception, event-based vision, and neural networks for motion analysis. Education : - PhD in Computer Science, University of Karlsruhe (1992), advised by Hans-Hellmut Nagel. - Diploma (Master's equivalent) in Electrical Engineering, National Technical University of Athens (1986). Research Interests : Daniilidis explores equivariant neural networks for structure from motion and robotics, event-based vision systems for low-latency perception, and 3D human motion capture using monocular and multi-sensor approaches. His work integrates geometric priors with deep learning to address challenges in SLAM, object pose estimation, and autonomous navigation. He also investigates robotic systems for tasks like object catching and fluid dynamics modeling, applying uncertainty-aware methods for robust decision-making. Articles Trends : Recent publications highlight advancements in event camera processing (e.g., EV-TTC, EqNIO), equivariant architectures for shape and motion analysis (SE(3)-Equivariant Networks), and multi-agent robotics with distributed perception systems. Collaborations with institutions such as ETH Zurich and Boston University reinforce his contributions to scalable and geometrically grounded AI solutions. Advising & Grants : He advises over 30 PhD students and postdocs, many now faculty or industry leaders. His grants include NSF awards (NCS-FO 2124355, CPS 2038873), ARL DCIST, and ONR funding, focusing on robotics, vision, and AI. His work often bridges theoretical insights (e.g., matrix permanent for localization) with practical robotic systems like the Tiercel drone. Labs & Teams : Affiliated with the GRASP Laboratory at Penn, a premier robotics & vision research center. Collaborates widely, including with the Robotics Lab at ETH Zurich and researchers at the University of Coimbra. His group develops open-source tools like EV-FlowNet and EventGAN for event-based vision.

Aukosh Jagannathمشاهده پروفایل

دانشیار

• Probability theory
• Mathematical foundations of data science
• Mathematical physics
• +۳ مورد دیگر

Aukosh Jagannath is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, with cross-appointments in the Department of Applied Mathematics and the Cheriton School of Computer Science. He is also an affiliate of the Perimeter Institute and Waterloo.AI. His research focuses on probability theory, mathematical foundations of data science, and mathematical physics, particularly in areas like high-dimensional learning, statistical-computational tradeoffs, and spin glasses. Before joining Waterloo, he held postdoctoral positions at Harvard University and the University of Toronto, and completed his PhD in mathematics at NYU’s Courant Institute under Gerard Ben Arous, alongside an undergraduate degree in math and physics from NYU. His work has been supported by grants from NSERC, the Canada Research Chairs program, the Canadian Foundation for Innovation, and the Ontario Research Fund. He has contributed to groundbreaking research on topics such as stochastic gradient descent dynamics, spectral alignment in neural networks, and the theoretical underpinnings of deep learning. Jagannath has advised numerous graduate and postdoctoral researchers, including current PhD students Taj Jones-McCormick and Varnan Sarangian, and former advisees like Aseem Baranwal (now at XTY Labs) and Yiming Xu (now at the University of Kentucky). His awards include an Outstanding Paper Award at NeurIPS 2022 for work on high-dimensional SGD dynamics.

Yuxin Chenمشاهده پروفایل

استاد

• Machine Learning
• High-Dimensional Estimation
• Nonconvex Optimization
• +۹ مورد دیگر

Yuxin Chen is a Professor at the Wharton School, University of Pennsylvania , appointed in both the Department of Statistics and Data Science and the Department of Electrical and Systems Engineering . He previously served as an Assistant Professor at Princeton University (2017–2021) and a Postdoctoral Scholar at Stanford University (2015–2017). Education: Ph.D., Electrical Engineering, Stanford University, 2015 M.S., Statistics, Stanford University, 2013 M.S., Electrical and Computer Engineering, UT Austin, 2010 B.S., Microelectronics, Tsinghua University, 2008 His research spans high-dimensional estimation , machine learning theory , nonconvex optimization , and statistical signal processing , with applications in medical imaging and computational biology . Recent work focuses on diffusion models , large language models (LLMs) , and reinforcement learning (RL) . His 15 most recent publications emphasize diffusion models (generalization, sampling efficiency, scientific applications), reinforcement learning (sample complexity, convergence theory), and nonconvex optimization (implicit regularization, statistical guarantees). Scientific Awards: Alfred P. Sloan Research Fellowship, 2022 Princeton SEAS Junior Faculty Award, 2021 Princeton Graduate Mentoring Award, 2020 ICCM Best Paper Award (Gold Medal), 2020 ARO Young Investigator Program (YIP) Award, 2020 AFOSR YIP Award, 2019 SIAM Activity Group on Imaging Science Best Paper Prize IEEE Transactions on Power Electronics Prize Paper Award (first place) He mentors PhD students and leads interdisciplinary research projects funded by agencies like the Army Research Office and Air Force Office of Scientific Research . His team develops frameworks such as LOCO Edit for controllable diffusion model editing and MagNet for power magnetics modeling .

John Wrightمشاهده پروفایل

دانشیار

• High-dimensional data analysis
• Signal processing
• Computer vision
• +۵ مورد دیگر

John Wright is an Associate Professor in the Department of Electrical Engineering at Columbia University's Fu Foundation School of Engineering and Applied Science, with additional affiliations in the Department of Applied Physics and Applied Mathematics and the Data Science Institute. He maintains his office at 408 Mudd Hall within the Data Science Institute and leads a research group focused on high-dimensional data analysis. Wright received his PhD in Electrical Engineering from the University of Illinois at Urbana-Champaign in October 2009, followed by a research position at Microsoft Research from 2009-2011 before joining Columbia University. His research focuses on developing algorithms for robustly recovering structured signal representations from incomplete and corrupted observations, with applications in imaging and vision. His research interests span high-dimensional data analysis, signal processing, computer vision, and optimization. Wright's work places particular emphasis on finding methods that perform well even when data is unreliable - noisy or corrupted - and which come with proofs of correctness. His research group develops tools for robustly analyzing high-dimensional data and applying them to practical problems such as image and video compression, face and object recognition. Wright's publication record shows a clear evolution from sparse representation and dictionary learning toward more complex deep learning architectures. His recent work focuses on geometric approaches to nonconvex optimization problems, blind deconvolution methods, and the theoretical foundations of deep networks. His publications consistently bridge theoretical guarantees with practical applications across computer vision and signal processing domains. Scientific Awards and Honors: 2009 Lemelson-Illinois Prize for Innovation for his work on face recognition 2009 UIUC Martin Award for Excellence in Graduate Research 2008-2010 Microsoft Research Fellowship 2012 COLT Best Paper Award (with Wang and Spielman) Wright has successfully mentored numerous PhD students who have gone on to prestigious positions at institutions including TTIC, Google, Rutgers University, and Boston University. He also co-authored the influential textbook 'High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications' published by Cambridge University Press in 2022, which has been used as course material at multiple leading universities. His research group maintains active collaborations across multiple institutions and regularly presents at top conferences including NeurIPS, ICML, CVPR, and COLT. The group holds weekly seminars (typically on Thursdays from 10-12 in 408 Mudd) where current research is discussed and developed.

Nicolas Verzelenمشاهده پروفایل

پژوهشگر

• Statistics
• Machine Learning
• Agroecological Data Analysis
• +۴ مورد دیگر

Nicolas Verzelen is a Senior Research Scientist at the MISTEA Laboratory within INRAE , affiliated with Université de Montpellier . His work bridges Statistics and Machine Learning , focusing on agroecological applications. Roles: Associate Editor for Annals of Statistics, Bernoulli, and Electronic Journal of Statistics; Editor-in-Chief for ESAIM: P&S Supervision: Mentoring 5 PhD/post-doc researchers; alumni include current professors at ENSAI, Centrale Supélec, and Telecom-Paris Research Interests: High-dimensional minimax theory, active learning, unsupervised learning (clustering, ranking), and applications to seed exchange networks and agroecology. His work addresses computational-statistical trade-offs in modern data science. Publication Trends: Recent outputs emphasize clustering algorithms, covariance adaptation, latent space modeling, community detection, and active learning frameworks. Collaborations span institutions like Université Paris-Saclay and Telecom-Paris. Teaching: Instructs Statistical Machine Learning (M2) at Université de Montpellier.

Mostafa Kavehمشاهده پروفایل

استاد

• Statistical Signal Processing
• Wireless Communications
• Image Processing
• +۳ مورد دیگر

Mostafa Kaveh is a Professor in the Department of Electrical and Computer Engineering at the University of Minnesota, College of Science and Engineering. His research spans statistical signal processing, wireless communications, image processing, and biomedical ultrasound imaging. He has held leadership roles including Department Head (1990-2005), Associate Dean for Research and Planning (2005-2018), and Dean of the College of Science and Engineering (2018-2022). His academic credentials include a B.S. and Ph.D. from Purdue University (1969, 1974) and an M.S. from UC Berkeley (1970). Kaveh’s research focuses on: Statistical signal processing for antenna arrays and mobile localization Wireless communication systems with multiple antennas and collaborative transmission Image restoration algorithms based on partial differential equations Medical image processing for tomography and reconstruction Signal processing applications in genomics and biotechnology Google Scholar publications highlight trends in array signal processing, direction-of-arrival estimation, wireless channel modeling, and medical imaging techniques. His work has influenced GRAPPA algorithms for MRI, space-time interference-canceling receivers, and genomic data analysis.

Shuyang Lingمشاهده پروفایل

استادیار

• Mathematics of Signal Processing
• Machine Learning
• Optimization
• +۷ مورد دیگر

Shuyang Ling is an Assistant Professor of Data Science at NYU Shanghai and a Global Network Assistant Professor at the Tandon School of Engineering, New York University. He joined NYU Shanghai in September 2019 as an Assistant Professor Faculty Fellow of Data Science, after serving as a Courant Instructor/Assistant Professor at the Courant Institute of Mathematical Sciences and Center for Data Science at NYU from 2017-2019. Dr. Ling received his PhD in Applied Mathematics from the University of California, Davis in 2017 under the supervision of Thomas Strohmer. He also earned an MS in Statistics from UC Davis in 2016 and completed his undergraduate studies in Mathematics and Applied Mathematics at Fudan University in Shanghai, China in 2012. Dr. Ling's research focuses broadly on the mathematics of data science, with particular interest in tackling inverse problems from engineering applications and extracting meaningful information from large-scale and heterogeneous datasets. His work spans a broad spectrum of subjects including: Optimization (convex and non-convex) Probability and statistics Computational harmonic analysis Numerical linear algebra Signal processing and machine learning theory His recent publications demonstrate a strong focus on synchronization problems, neural collapse phenomena, and optimization landscapes. He has made significant contributions to understanding the theoretical foundations of spectral methods, non-convex optimization, and convex relaxations in data science applications. His work often bridges theoretical guarantees with practical algorithms for problems in signal processing and machine learning. Dr. Ling has received several prestigious awards and grants including the SIAM Student Paper Prize in 2017, Shanghai Eastern Scholar for Young Professionals (2019), Shanghai Rising Star Program (A-type) (2024), and grants from the Natural Science Foundation of Shanghai and National Key R&D Program of China. He is actively involved in mentoring the next generation of researchers, currently advising three Ph.D. students at NYU Shanghai: Jiayang Yin (co-advised with Prof. Mathieu Lauriere), Wanli Hong, and Ziliang Samuel Zhong. He has also supervised numerous undergraduate thesis projects at NYU Shanghai since 2020. Dr. Ling is part of the Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning at NYU Shanghai, where he organizes seminars and reading groups on data science and machine learning.

Lu Ganمشاهده پروفایل

مدرس ارشد

• Signal Processing
• Compressed Sensing
• Machine Learning
• +۱۰ مورد دیگر

Dr. Lu Gan is a Senior Lecturer in the Department of Electronic and Computer Engineering at Brunel University London, within the College of Engineering, Design, and Physical Sciences. She earned her B.Eng and M.Eng in Electronic and Information Engineering from Southeast University, China, in 1998 and 2000, followed by a Ph.D. in Information Engineering from Nanyang Technological University, Singapore, in 2004. Before joining Brunel in 2008, she held faculty positions at the University of Newcastle, Australia (2004-2006) and the University of Liverpool, UK (2006-2007). Her research spans fundamental signal processing theories, machine learning, and applications in image/video coding, non-destructive terahertz/ultrasound imaging, wireless communications, and sparse antenna arrays. Education: B.Eng (Electronic and Information Engineering), Southeast University, China (1998) M.Eng (Electronic and Information Engineering), Southeast University, China (2000) Ph.D (Information Engineering), Nanyang Technological University, Singapore (2004) Her research focuses on structured sparse signal processing for infrared/terahertz systems, super-resolution in non-destructive imaging, deep learning for terahertz data, non-orthogonal pilot design for 5G systems, and separation of singing voice from music. She has secured funding from EPSRC, Innovate UK, BBSRC, UK Atomic Energy Authority, and TWI. She actively contributes to academic service as an Associate Editor for IEEE Signal Processing Letters, IEEE Transactions on Circuits and Systems-I, and as a Meta Reviewer for ICASSP 2025. Her work has been recognized with a Best Paper Award from the Journal of The British Blockchain Association in 2022 and a Gold Medal at the IEEE Audio and Acoustic Signal Processing Challenge (DCASE) in 2022. Dr. Gan also serves as a reviewer for top journals like IEEE Transactions on Information Theory and IEEE Transactions on Signal Processing, and participates in grant panels for the Royal Society and EPSRC. Recent publications emphasize cross-domain speech enhancement architectures, terahertz data reconstruction via spatio-temporal dictionary learning, and coprime array designs using Chinese remaindering over quadratic fields. Her work bridges compressive sensing, lattice structures, and practical applications in healthcare and communication systems. She is a Senior Member of IEEE, Fellow of the Higher Education Academy (UK), and actively contributes to departmental leadership as Course Director for MSc Wireless Communication Systems, Level 3 Coordinator, and Social Media Administrator.

Felix Krahmerمشاهده پروفایل

دانشیار

• Optimization and Data Analysis
• Compressed Sensing
• Unlimited Sampling
• +۷ مورد دیگر

Prof. Dr. Felix Krahmer is an Associate Professor for Optimization and Data Analysis at the Department of Mathematics, Technical University of Munich (TUM) . His research focuses on mathematical foundations of data science, with expertise in Compressed Sensing Unlimited Sampling Quantization of Signals and Images Neural Networks Uncertainty Quantification Low-Rank Recovery He leads the Data Science research group within TUM's School of Computation, Information and Technology, collaborating with institutions like TU Berlin, RWTH Aachen, and Technion. His recent publications span topics like Robust blind deconvolution under adversarial noise Kronecker-structured dimensionality reduction Hysteresis in unlimited sampling systems Efficient sparse FFT algorithms Notable scientific awards include two August-Wilhelm-Scheer Visiting Professorships (2016, 2019) for hosting international collaborators. Former and current students include Olga Graf Dominik Stöger Claudio Verdun Felipe Pagginelli Patricio Juana Kostin He has co-organized workshops at SAMPTA, Oberwolfach, and ISIT, serving as associate editor for journals including SIAM Journal on Matrix Analysis and Frontiers in Signal Processing.

Weiping Peiمشاهده پروفایل

استادیار

• Security and Privacy
• Crowdsourcing
• Machine Learning
• +۳ مورد دیگر

Weiping Pei is an Assistant Professor in the School of Cyber Studies at The University of Tulsa, specializing in security and privacy, crowdsourcing, machine learning, and human-computer interaction. Her work bridges technical security mechanisms with human behavioral aspects to develop practical privacy-preserving systems. Education: Ph.D. in Computer Science, Colorado School of Mines (2022) M.Eng. in Software Engineering, Sun Yat-sen University (2017) B.Eng. in Microelectronics, Sun Yat-sen University (2015) Dr. Pei's research centers on three interconnected pillars: secure and privacy-preserving crowdsourcing (with focus on receipt transcription systems and third-party privacy), robust machine learning (particularly adversarial attacks against vulnerability detection and content-preserving text manipulation), and usable security (including GDPR policy analysis and online grooming detection). Her approach uniquely integrates human behavioral modeling with cryptographic and machine learning techniques to address real-world security gaps in crowd-powered systems. Analysis of her 2023-2025 publications reveals a strategic shift toward AI-driven security solutions, particularly using LLMs for behavioral analysis in digital forensics and adversarial machine learning for vulnerability assessment. Her work consistently targets high-impact venues like IEEE S&P and ACM CCS, demonstrating both theoretical rigor and practical applicability in privacy policy compliance and crowdworker security. Scientific Awards: NSF CAREER Award (2025) for foundational cybersecurity research NSF SaTC 2.0 RES Award (2025) for privacy-preserving technologies NSF CRII Grant recipient (2023) Distinguished Rath Award nominee for societal-impact PhD thesis (2022) Outstanding CS Ph.D. Student Award at Mines (2021) Grace Hopper Celebration Scholarship (2018) Dr. Pei actively mentors four students across degree levels while leading multiple NSF-funded projects totaling $1.3+ million. Her research group develops tools for privacy policy analysis (PolicyChecker), adversarial vulnerability testing (AdVul), and online safety monitoring. She teaches core cybersecurity courses including Data Security and Cryptography, with future work focusing on AI security foundations through her NSF CAREER grant.

Victor Preciadoمشاهده پروفایل

دانشیار

• Network Science
• Dynamical Systems
• Data Science
• +۳ مورد دیگر

Victor Preciado is an Associate Professor at the University of Pennsylvania, holding appointments in the Departments of Electrical & Systems Engineering and Computer & Information Science . He is affiliated with the Warren Center for Network & Data Sciences , the PRECISE Center , and the Applied Math and Computational Science graduate group . His research bridges Network Science , Dynamical Systems , and Data Science , focusing on modeling, analysis, and control of complex networked systems. Ph.D. in Electrical Engineering and Computer Science from MIT (supervised by Prof. George Verghese) Visiting scholar at UC Berkeley, Santa Fe Institute, and Courant Institute (NYU) Postdoctoral researcher at GRASP Lab (working with Prof. Ali Jadbabaie) His research interests include epidemic modeling and control , socio-technical networks , network controllability , and data-driven optimization . Recent work explores contact-aware robotics and operator learning using attention mechanisms. His publications from 2022–2024 highlight advancements in robust control , epidemic tracking , and hypergraph neural networks , with applications in pandemic management , robotics , and temporal network analysis . Scientific awards include: 2024: IEEE Robotics and Automation Society Best Paper Award finalist (for Stabilization of Complementarity Systems ) 2022: IEEE Control Systems Magazine Best Paper Award (retrospectively for Analysis and Control of Epidemics ) 2017: National Science Foundation CAREER Award 2019: IEEE Transactions on Network Science and Engineering Best Paper Award runner-up He has served as Associate Editor for IEEE Transactions on Network Science and Engineering and IEEE Transactions on Control of Networked Systems , and as Guest Editor for special issues on pandemics and control of epidemics in journals like Annual Reviews in Control and SIAM Journal on Control and Optimization .

Cheng Maoمشاهده پروفایل

استادیار

• statistical inference on random graphs
• mathematics of data science
• mathematical statistics
• +۲ مورد دیگر

Cheng Mao is an Assistant Professor in the School of Mathematics at the Georgia Institute of Technology (Georgia Tech), with office location in Skiles 102C and mailing address at 686 Cherry Street, Atlanta, GA 30332 USA. His position is active as of 2025, with no indication of part-time status or retirement. Education: B.S. and M.A. in Mathematics, University of California, Los Angeles (UCLA), 2013 Ph.D. in Mathematics and Statistics, Massachusetts Institute of Technology (MIT), 2018 (Advisor: Philippe Rigollet) Postdoctoral Researcher, Yale University, 2018-2019 (with Yihong Wu) Cheng Mao's research centers on the mathematics of data science, specifically statistical inference for random graphs. His work bridges mathematical statistics, applied probability, and theoretical computer science to address fundamental problems in graph matching, community detection, and matrix estimation under structural constraints like total positivity. He investigates algorithmic efficiency, information-theoretic limits, and the detection-recovery gap in planted models. His publication trends (2020-2025) reveal deep specialization in spectral methods for graph matching (e.g., Erdős-Rényi graphs, noise robustness) and combinatorial approaches to planted structures (dense cycles, subhypergraphs). Key themes include leveraging low-degree polynomials for detection, tree counting for network correlation, and method-of-moments techniques for permutation learning. Matrix estimation under Monge and total positivity constraints forms another consistent thread. Scientific Awards: No scientific awards mentioned in the provided text Cheng Mao advises three Ph.D. students at Georgia Tech: Jingyi (Joy) Zhang (co-advised with Debankur Mukherjee), Shenduo Zhang, and Abhishek Dhawan (Ph.D. 2024, now postdoc at UIUC, co-advised with Anton Bernshteyn). Timothy Wee joined as a postdoc in 2024. The text contains no references to research grants or external funding sources. No laboratories, research teams, or collaborative groups are specified in the source material.