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Aayush JainView profile

Assistant Professor

• Cryptography
• Theoretical Computer Science
• Applied Cryptography
• +3 more

Aayush Jain is an Assistant Professor in the Computer Science Department at Carnegie Mellon University. Previously, he was a Postdoctoral Fellow at NTT Research and a PhD student at UCLA, advised by Professor Amit Sahai. His work bridges theoretical and applied cryptography with core computer science principles. Education PhD in Computer Science, University of California, Los Angeles (UCLA) Postdoctoral Fellowship, NTT Research Research Focus His research explores: foundational cryptography, indistinguishability obfuscation, functional encryption, lattice-based cryptography, secure multi-party computation, and post-quantum security. Work emphasizes rigorous theoretical frameworks with practical implications. Publication Trends Recent articles (2021-2024) demonstrate consistent focus on cryptographic primitives, obfuscation techniques, and security reductions. Dominant venues include CRYPTO, EUROCRYPT, FOCS, and STOC with emerging work in machine learning interfaces. Awards Best Paper Award at STOC 2021 for foundational contributions to indistinguishability obfuscation Advising and Collaboration Current PhD advisees: Alper Cakan, Quang Dao (co-advised), Sagnik Saha, Noah Singer (co-advised). Mentored postdocs: Mitali Bafna (2022-2023) and Rex Fernando (2022-2023). Teaches graduate courses in cryptography and theoretical tools. Leadership Leads the CMU Cryptography research group; organized the CMU Cryptography Workshop. Program committee member for FOCS, TCC, ITCS, and ICALP.

Garvesh RaskuttiView profile

Associate Professor

• Statistical Machine Learning
• Optimization Theory
• Graphical and Network Modeling
• +5 more

Garvesh Raskutti is an Associate Professor in the Department of Statistics at the University of Wisconsin–Madison, affiliated with the School of Computer, Data & Information Sciences. He holds joint affiliations with the Departments of Computer Science, Electrical and Computer Engineering, and the Wisconsin Institute of Discovery Optimization Group. His research focuses on statistical machine learning, optimization, graphical/network modeling, and information theory, with applications to systems biology and neuroscience. Education: MEng from the University of Melbourne (2008), PhD from UC Berkeley (2012, advised by Martin Wainwright and Bin Yu), and postdoctoral work at SAMSI. Teaching awards include Honored Instructor (2014, 2017) and Madison Teaching and Learning Excellence Fellow (2015). He advises multiple PhD and undergraduate students, including Yuan Li, Hyebin Song, and Lili Zheng. His grants include NGA HM0476-17-1-2003 (Co-PI), ARO W911NF-17-1-0357 (Co-PI), and NSF-DMS 1407028 (Sole PI). Research interests span large-scale statistical inference, computational-statistical trade-offs, and applications in systems biology and neuroscience. His work bridges optimization (e.g., gradient descent, convex regularization), high-dimensional regression, and network analysis. Recent trends in publications emphasize methods for non-convex optimization, sparse models, and network structure learning. Awards include teaching recognition and grants in statistical methodology. Advising spans theoretical and applied projects, with former students like Gunwoong Park (now at University of Korea). Collaborations include interdisciplinary teams in machine learning and signal processing.

Arian MalekiView profile

Associate Professor

• Compressed Sensing
• Signal Processing
• Statistical Inference
• +6 more

Arian Maleki is an Associate Professor in the Department of Statistics at Columbia University, affiliated with the Faculty of Arts and Sciences and the Foundations of Data Science Center. He holds a PhD from Stanford University (2010) and previously served as a postdoctoral scholar at Rice University. His research focuses on statistical inference, signal processing, and machine learning, with particular emphasis on compressed sensing, high-dimensional statistics, and algorithm design. Key areas include noise mitigation, image reconstruction, and the theoretical analysis of algorithms for inverse problems. Recent work highlights include studies on speckle noise challenges, phase transitions in compressed sensing, and certified data removal techniques. His contributions bridge theory and application, often addressing practical computational and statistical challenges in imaging and signal processing. No scientific awards are explicitly listed. Research outputs emphasize foundational advancements in statistical methodologies and algorithmic frameworks for high-dimensional data analysis. Labs/teams: Active involvement in the Foundations of Data Science Center at Columbia University.

Scott T. M. DawsonView profile

Assistant Professor

• Fluid Mechanics
• Dynamical Systems
• Control Theory
• +5 more

Scott T. M. Dawson is an Assistant Professor in the Mechanical, Materials, and Aerospace Engineering Department at Illinois Institute of Technology (Illinois Tech). He holds positions in the Armour College of Engineering and leads research at the intersection of fluid mechanics, dynamical systems, control theory, and data science. His work focuses on extracting dynamic models from large datasets to analyze and control turbulent flows and unsteady aerodynamic systems. Education includes a Ph.D. and M.A. from Princeton University (2017, 2013), and B.Eng. and B.S. degrees from Monash University (2010, 2009). Prior to Illinois Tech, he was a postdoctoral scholar at Caltech’s Graduate Aerospace Laboratories under Prof. Beverley McKeon. Research interests emphasize reduced-order modeling, data-driven techniques for fluid flows, and flow control applications. His group’s work is supported by NSF, AFOSR, and DOE grants. Recent projects include sparsity-promoting methods for flow analysis, wavelet-based resolvent analysis, and neural network-driven flow control systems. Publications span over 60 peer-reviewed articles, with a focus on turbulence modeling, transient flow dynamics, and machine learning integration in fluid mechanics. Key contributions include novel algorithms for isolating amplification mechanisms in wall-bounded flows and robust neural network frameworks for closed-loop flow stabilization. Grants and collaborations include multi-year NSF CAREER funding for automated distillation of coherent flow structures. Ongoing efforts explore time-localized spectral methods, nonlinear dimensionality reduction, and hydrogen decarbonization in vehicular systems.

Yongshan DingView profile

Assistant Professor

• Quantum Computing
• Computer Architecture
• Quantum Algorithms
• +4 more

Yongshan Ding is an Assistant Professor of Computer Science and Applied Physics at Yale University. He leads the Quantum Systems Lab (QSL) and directs Yale's Quantum Science and Engineering Certificate program. Affiliated with the Yale Quantum Institute (YQI) and Computer Systems Lab (CSL), his research focuses on quantum computing systems spanning algorithms, architecture, and hardware/software co-design. Dr. Ding earned his Ph.D. from the University of Chicago and a B.Sc. from Carnegie Mellon University. He has received prestigious awards including the Siebel Scholarship (2020) and William Rainey Harper Dissertation Fellowship (2020). Research Interests: Quantum computing architectures, noise-resilient quantum algorithms, error correction methods, quantum compilation, hardware-software co-design, and NISQ system optimization. Editorial Roles: Editor at Quantum journal and Associate Editor at ACM Transactions on Quantum Computing . Labs: Founder of Yale's Quantum Systems Lab (QSL) and contributor to the Computer Systems Lab (CSL). Awards: Siebel Scholarship (2020) William Rainey Harper Dissertation Fellowship (2020) QCE Best Paper Award (2024) IEEE Micro Top Picks Honorable Mention (2023, 2020) IBM Q Best Paper Award, First Prize (2020) Mathematics Competition Runner Up (2016)

Melanie Matchett WoodView profile

Professor

• Number Theory
• Arithmetic Statistics
• Arithmetic Geometry
• +5 more

Melanie Matchett Wood is the William Caspar Graustein Professor of Mathematics at Harvard University. Her research spans number theory, arithmetic statistics, algebraic geometry, and probability theory, with a focus on distributions of class groups, Galois groups of unramified extensions, and random algebraic structures. She has been supported by prestigious awards including the Packard Fellowship, the NSF Waterman Award, and the MacArthur Fellowship. Her work connects number theory to topology through function field analogs, studying moduli spaces of curves and their statistical properties. She has made significant contributions to understanding the universality of random matrix cokernels and their applications to sandpile groups of graphs. Her editorial roles include the Journal of the American Mathematical Society and Algebra and Number Theory . Recent publications emphasize arithmetic topology, proving universality theorems for 3-manifold groups, and developing new heuristics for class group torsion. She organizes seminars on arithmetic statistics and topology-number theory interactions. Her teaching includes advanced courses like Algebraic Number Theory and Class Field Theory, with research supervision spanning PhD and undergraduate projects. Scientific Awards: Packard Fellowship for Science and Engineering National Science Foundation Waterman Award MacArthur Fellowship

Sandra KeiperView profile

Lecturer

• Compressed Sensing
• Sparse Signal Recovery
• Numerical Linear Algebra
• +5 more

Sandra Keiper is a Lecturer at the Institute of Mathematics within Faculty II - Mathematics and Natural Sciences at Technical University of Berlin. She has held academic positions since at least 2011, including roles as Tutor, Assistant, and Lecturer, with teaching responsibilities in Analysis, Linear Algebra, and Partial Differential Equations for both mathematicians and engineers. Research interests include: Compressed Sensing and Sparse Signal Recovery Numerical Linear Algebra with applications to high-dimensional data Wavelet and curvelet transforms for geometric multiscale analysis Approximation theory for finite-valued and cartoon-like functions Deep learning and graph approximation techniques Professional activities : Active in teaching since 2011 (Analysis I-III, Functional Analysis, Integral Transforms) Supervising theses since 2015 on topics like Compressed Sensing and Deep Learning Invited lectures at Caltech, ETH Zurich, and Alan Turing Institute Research stays at Hausdorff Institute, ETH Zurich, and Duke University

David SteurerView profile

Associate Professor

• Theoretical Computer Science
• Algorithms
• Sum-of-Squares Method
• +5 more

David Steurer is an Associate Professor in the Department of Computer Science at ETH Zurich, where he leads the Institute for Theoretical Computer Science. His research bridges theoretical computer science, mathematics, and practical applications in machine learning and statistics. Steurer has made significant contributions to the understanding of sum-of-squares methods, semidefinite programming, and high-dimensional estimation problems. Steurer earned his B.Sc. & M.Sc. in Computer Science from Saarland University in 2006, followed by a Ph.D. in Computer Science from Princeton University in 2010 under the supervision of Sanjeev Arora. His doctoral dissertation, "On the Complexity of Unique Games and Graph Expansion," received an Honorable Mention for the ACM Doctoral Dissertation Award in 2011. Steurer's research focuses on algorithm design through mathematical programming relaxations, particularly semidefinite programming and the sum-of-squares method. His work addresses computational complexity of high-dimensional estimation problems including tensor decomposition, clustering, Gaussian mixture models, and stochastic block models. He also investigates algorithmic aspects of robustness and differential privacy, especially for estimation tasks. His theoretical contributions have practical implications for machine learning and data analysis in the presence of noise and adversarial conditions. Analysis of Steurer's recent publications reveals a consistent trajectory in advancing the sum-of-squares framework for high-dimensional estimation and robust statistics. His work demonstrates how sophisticated mathematical programming techniques can provide certifiable guarantees for challenging problems in machine learning. The research spans theoretical foundations while maintaining relevance to practical applications, particularly in scenarios involving corrupted or noisy data. A notable trend is the extension of sum-of-squares methods to handle increasingly complex statistical models while providing rigorous performance guarantees. ERC Consolidator Grant (2019) Michael and Sheila Held prize (2018, with Raghavendra) Invited speaker at International Congress of Mathematicians (2018) STOC Best Paper Award (2015) Alfred P. Sloan Research Fellowship (2014) NSF CAREER Award (2014) Microsoft Research Faculty Fellowship (2014) FOCS Best Paper Award (2010) Steurer has advised numerous PhD students and postdocs who have gone on to prominent positions, including Sam Hopkins (now faculty at MIT), Jonathan Shi, and Aaron Potechin. His research is supported by multiple prestigious grants, including an ERC Consolidator Grant, an NSF CAREER Award, and a Microsoft Research Faculty Fellowship. He has served on numerous program committees for top theoretical computer science conferences and has been recognized for his service to the community through roles such as internal member of the Scientific Advisory Committee of the Institute for Theoretical Studies at ETH Zurich. As head of the Institute for Theoretical Computer Science at ETH Zurich, Steurer leads a research group focused on advancing the theoretical foundations of computer science with particular emphasis on mathematical optimization methods. His team explores the boundaries of what can be efficiently computed in high-dimensional settings, with applications spanning machine learning, statistics, and data science. The group maintains strong connections with both theoretical and applied researchers across ETH and the broader academic community.

Ming-Jun LaiView profile

Professor

• Multivariate Splines
• Sparse Solutions of Underdetermined Linear Systems
• Wavelet Theory
• +8 more

Ming-Jun Lai is a Professor in the Department of Mathematics at the University of Georgia. His career spans decades, focusing on multivariate splines, sparse solutions of linear systems, wavelet theory, and their applications in numerical analysis and machine learning. Education: Lai received his Ph.D. from Texas A&M University and completed postdoctoral training at the University of Utah. He has supervised 22 Ph.D. students and two current Ph.D. candidates. Multivariate Splines: Applied to scattered data fitting, numerical PDE solutions, image enhancement, and surface design. Sparse Solutions: Used in compressed sensing, low-rank matrix recovery, and graph clustering. Wavelet Theory: Construction of biorthogonal and tight wavelet frames for image edge detection. Optimal Transport: Numerical solutions for Monge-Ampère equations. Research Trends (2025–2023): Recent work includes interpolating space curves with geometric continuity, spherical spline smoothing, and applications in machine learning, particularly graph clustering and optimal control in biological systems. Scientific Awards: UGA Research Medal (2002) McCay Award (2013) Advisees: Lai has mentored 24 Ph.D. students, including Zhaiming Shen (2024), Jinsil Lee (2023), and current students Valerio Palamra and Ye Tian. Laboratory & Collaborations: He collaborates with institutions like Georgia Tech, UCLA, and Zhejiang University, applying splines in aerospace engineering and biomedical imaging.

• Computer Networks
• Internet of Things (IoT)
• Sensor Networks
• +7 more

Jorge Garcia Vidal is a Professor in the Department of Computer Architecture at the School of Computer Science, Universitat Politècnica de Catalunya (UPC). He is a key member of the CNDS - Computer Networks and Distributed Systems research group, with a sustained record of research activity from the late 1980s to the present, including publications projected into 2025. His work bridges theoretical network performance analysis and applied IoT systems, particularly in environmental monitoring. His research interests center on Computer Networks , Internet of Things (IoT) , Sensor Networks , and Data Quality in IoT . He has made significant contributions to ATM network performance, medium access control, and traffic modeling. More recently, his focus has shifted to air quality monitoring using low-cost sensor networks, employing techniques in Graph Signal Processing , Machine Learning , and Anomaly Detection to improve data reliability and estimate pollutants like black carbon. The recent article trends show a strong emphasis on developing data-driven frameworks, virtual sensors, and robust models for environmental IoT platforms. His work integrates advanced signal processing and machine learning to address the challenges of heterogeneous, low-cost sensor data in urban settings. His scientific achievements have been recognized with awards including the Premio Extraordinario de Doctorado and the Premio Mejor Tesis Doctoral . He has advised several doctoral students, including Pau Ferrer-Cid, David Fusté Vilella, Steluta Iordache, and Julian David Morillo Pozo. He is actively involved in numerous competitive and non-competitive R&D projects, such as those related to digital twins, IoT platforms for smart cities, and nature-based urban solutions, often funded by state and regional programs. He collaborates extensively within UPC and with external partners. His research is conducted primarily within the CNDS research group at UPC, a collaborative environment focused on computer networks and distributed systems, with connections to broader initiatives in smart cities and environmental monitoring.

Maneesh AgrawalaView profile

Professor

• Computer Graphics
• Human-Computer Interaction
• Visualization
• +7 more

Maneesh Agrawala is the Forest Baskett Professor of Computer Science and Director of the Brown Institute for Media Innovation at Stanford University. He is also a consulting AI Scientist at Roblox. His research lies at the intersection of computer graphics, human-computer interaction (HCI), and visualization, with a focus on cognitive design principles for improving audio/visual media. He leads a vibrant research group and has advised numerous PhD students and postdocs. His research interests include: Computer Graphics Human-Computer Interaction Visualization and Visual Communication Cognitive Design Principles Generative AI and Diffusion Models Interactive Video and Sketch-based Interfaces Data and Information Visualization His recent publications (2023–2025) reflect a strong trend toward leveraging generative models—particularly diffusion models—for video and image synthesis, editing, and personalization. Key themes include controllable generation (e.g., SparseCtrl, ControlNet), sketch-to-image translation, relightable texturing, and tools that enhance visual storytelling and data communication. His work integrates cognitive science with computational tools to build systems that support human creativity and understanding. His scientific honors include: MacArthur Foundation Fellowship (2009) Alfred P. Sloan Foundation Fellowship (2007) NSF CAREER Award (2007) SIGGRAPH Significant New Researcher Award (2008) Allen Distinguished Investigator Award (2014) Induction into the SIGCHI Academy (2021) ACM Fellow (2022) Okawa Foundation Research Grant (2006) A dedicated mentor, Agrawala has advised a large cohort of students and postdocs, many of whom have gone on to influential positions in academia and industry. His lab develops tools for video editing (e.g., AnimateDiff, ControlNet), sketch-based design, and visualization (e.g., EmphasisChecker), often bridging theory with practical applications in media and education. He continues to be a leading figure in visual computing and interactive systems.

• Reinforcement Learning
• Bandit Algorithms
• Learning Theory
• +4 more

Claire Vernade is a Group Leader at the University of Tübingen within the Cluster of Excellence Machine Learning for Science. She holds an Emmy Noether award (2022) and an ERC Starting Grant (2024) for her projects FoLiReL and ConSequentIAL , focusing on theoretical reinforcement learning and non-stationary environments. Education: PhD from Telecom ParisTech (2017) Post-doctoral researcher at University of Magdeburg (2018) Her research bridges sequential decision making, bandit problems, and reinforcement learning theory. Recent work explores lifelong learning, distributional RL, and game-theoretic approaches to PCA, emphasizing mathematical rigor and algorithmic innovation. Recent publications highlight trends in non-stationary RL , continual learning , and bandit algorithms with complex feedback structures. Key subfields include meta-learning, adaptive control, and theoretical guarantees in dynamic programming. Scientific Awards: Emmy Noether Award (2022) ERC Starting Grant (2024) Outstanding Paper Award & Oral Presentation, ICLR (2021) She mentors PhD and master's students in theoretical machine learning, with current advisees including Nicolas Nguyen, Onno Eberhard, and Ziyad Sheebaelhamd. Her lab actively recruits candidates in bandit algorithms and RL theory through the IMPRS-IS and ELLIS doctoral programs. Claire co-leads diversity initiatives like Tübingen Women in Machine Learning and Women in Learning Theory, advocating for inclusivity in AI research. She has organized workshops at ICML and EWRL, and contributed to union activism in the tech industry.

Mohsen PourahmadiView profile

Professor

• Time Series Analysis
• Multivariate Statistics
• Covariance Estimation
• +7 more

Mohsen Pourahmadi is a Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. His research focuses on developing methodologies for modeling covariance matrices in multivariate and time series data, with applications to financial analysis, longitudinal studies, neuroeconomics, and high-dimensional data. Key tools include graphical lasso algorithms, Cholesky decomposition, and Bayesian approaches. He emphasizes extending generalized linear models (GLM) to covariance matrix estimation, leveraging prediction theory and stochastic processes. Education details are not explicitly provided in the text. His work spans theoretical advancements in covariance estimation, such as sparse VAR models, nonstationary process analysis, and regularized multivariate regression. He has contributed to applications like detecting cyber attacks on infrastructure systems and analyzing breast cancer data through Bayesian networks. Research interests include time series graphical models, antedependence models for longitudinal data, and regularization techniques for high-dimensional covariance matrices. His recent work explores fused-lasso penalties, Bayesian correlation matrix estimation, and stationary subspace analysis. Pourahmadi has authored numerous articles on topics ranging from multivariate volatility modeling to nonparametric covariance estimation, emphasizing both computational efficiency and theoretical rigor.

Zoran BudimlićView profile

Associate Professor

• High Performance Computing
• Parallel Computing
• Compiler Optimizations
• +3 more

Zoran Budimlić is an Instructional Associate Professor in the Department of Computer Science and Engineering at Texas A&M University. He also serves as Director of Undergraduate Studies for Galveston. His roles include teaching and academic leadership in computer science education and high-performance computing. He holds a Ph.D. in Computer Science from Rice University (2001) and a B.S. in Computer Science and Engineering from the University of Belgrade (1994). His research interests focus on high-performance and parallel computing, compiler optimizations, programming languages, runtime systems, and high-level programming models. He emphasizes improving educational practices in computer science through innovative methods and curricula. His recent publications span parallel algorithms, task parallelism integration with MPI, and compiler optimizations for performance. Earlier work includes contributions to Java runtime optimization and static analysis techniques. Zoran Budimlić has no explicitly listed scientific awards or grants in the provided text, but his contributions to parallel computing and compiler design are notable. He advises students in these areas but no names are provided in the text.

Tianyu GuanView profile

Assistant Professor

• Sports Analytics
• Machine Learning
• Functional Data Analysis
• +6 more

Tianyu Guan is an Assistant Professor in the Department of Mathematics and Statistics at York University, Faculty of Science. He previously served as an Assistant Professor at Brock University and joined York University in 2024. He holds a PhD in Statistics from Simon Fraser University (2020), an MSc in Actuarial Science from the same institution (2014), and a BSc in Statistics from Jilin University (2011). PhD in Statistics, Simon Fraser University, 2020 MSc in Actuarial Science, Simon Fraser University, 2014 BSc in Statistics, Jilin University, 2011 His research centers on sports analytics, functional data analysis, and nonparametric statistics, with strong applications in machine learning and data science. He applies statistical methodologies to understand sports performance, player behavior, and game dynamics. His work also extends to theoretical developments in sparse modeling and functional regression. The recent publications highlight a clear trend toward integrating advanced statistical techniques with real-world sports and entertainment data. His work combines functional data analysis, machine learning, and probabilistic modeling to extract insights from complex longitudinal and high-dimensional datasets. Topics span soccer, rugby, football, and movie reviews, demonstrating interdisciplinary reach. While no formal scientific awards are listed in the provided text, his publications in high-impact journals such as Annals of Applied Statistics and Statistics and Computing reflect strong academic recognition. Tianyu Guan actively advises multiple graduate students at both MSc and PhD levels, primarily at Brock and Simon Fraser Universities. His teaching portfolio includes advanced courses in nonparametric statistics, sampling theory, and experimental design at the undergraduate and graduate levels. He has not received external grant information in the provided text, but his research output suggests active engagement in funded or independent research projects. He leads methodological and applied research in sports analytics, often co-supervising students with colleagues across institutions. His lab or research group appears focused on developing and applying statistical tools for performance analysis and decision-making in sports, supported by computational implementations such as the R package ngr .