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Jan-Jo ChenView profile

Professor

• Machine Learning
• Deep Learning
• Quantum Computing
• +7 more

Jan-Jo Chen is a Professor in the Department of Mathematics & Computer Science at Chicago State University, part of the College of Arts & Sciences. His research spans computer science, data analytics, and quantum information, with a strong focus on machine learning, deep learning, and high-performance computing. His research interests include machine learning, deep learning, quantum computing, supercomputing, concurrent and parallel processing, database security, and educational technology. He has developed models for predictive analytics in wealth distribution, facial recognition, curriculum recommendation, and forest cover classification, often leveraging decision trees and neural networks. The recent publications highlight a consistent trend in applying machine learning to diverse domains such as economics, education, and environmental science. His work increasingly emphasizes quantum computing education and interdisciplinary convergence, particularly through NSF-funded initiatives. Topics like hyperparameter tuning, multivariate analysis, and state-dependent modeling reflect a deep engagement with both theoretical and applied aspects of data science. Scientific Awards: No specific awards are listed in the provided text. Dr. Chen has been actively involved in securing and managing research grants, particularly from the National Science Foundation (NSF). He has served as Co-Principal Investigator on projects like PRELS and ExpandQISE NxGenDQ, and as Senior Personnel on the NSF Engines proposal. He also led the CSER Coordinator role at Chicago State University, supporting STEM education and research. While student advisees are not named, his involvement in undergraduate research and curriculum development suggests active mentorship. He is associated with the Center for STEM Education & Research (CSER) at Chicago State University and contributes to national initiatives in quantum education (QuSTEAM, ExpandQISE) and regional innovation (NSF Engines). These collaborations indicate leadership in convergent STEM education and workforce development.

Stavros SintosView profile

Assistant Professor

• Databases
• Data Mining
• Computational Geometry
• +3 more

Stavros Sintos is an Assistant Professor in the Department of Computer Science at the University of Illinois Chicago. He previously held a postdoctoral position at the University of Chicago and earned his Ph.D. and B.S. in Computer Science from Duke University and the University of Ioannina, respectively. Education: Ph.D. in Computer Science (Duke University, 2020), B.S. in Computer Science (University of Ioannina, Greece). His research focuses on designing efficient algorithms for databases, data mining, and computational geometry. Specific interests include geometric indexes with theoretical guarantees, approximate query processing, top-k queries, summarization queries, and join queries. He explores intersections with algorithmic fairness and temporal data analysis. Recent publications highlight innovations in clustering algorithms , temporal join queries , GPU-accelerated visualization , and fairness-aware systems . Key venues include SIGMOD, VLDB, PODS, and ICML-affiliated conferences. Scientific Awards: James B. Duke Fellowship; nominee for Outstanding Ph.D. Dissertation Award (2019-2020). He mentors talented students like Ph.D. candidate Aryan Esmailpour and collaborates with teams at UIC and Duke University. His work bridges theoretical guarantees with practical implementations for real-world data challenges.

Lenore ZuckView profile

Research Professor

• Theorem Proving
• Formal Methods
• Translation Validation
• +1 more

Lenore Zuck is a Research Professor at the University of Illinois at Chicago in the Department of Computer Science. Her work focuses on formal methods , theorem proving , and translation validation , with applications in security protocols and compiler verification . She has contributed to runtime verification, probabilistic model checking, and abstract interpretation. Her research spans software verification , distributed systems , and secure web development . She has participated in major conferences like TACAS , VMCAI , CSFW , and workshops on formal methods . Recent publications address parameter tampering defenses , transactional memory verification , and declarative secure languages . She is affiliated with the Analysis of Computer SYStems group at NYU and has collaborated on projects like the Compiler Validation Project . Her work often integrates theoretical foundations with practical applications in system design and security.

Omri WeinsteinView profile

Assistant Professor

• Theoretical Computer Science
• Data Structures
• Information Theory
• +12 more

Omri Weinstein is an Assistant Professor in the Department of Computer Science at Columbia University. His research bridges Information Theory, Data Structures, and Optimization, focusing on dynamic data structures and dimensionality-reduction techniques to accelerate optimization and search. He received his PhD from Princeton University and was a Simons Society Junior Fellow at the Courant Institute (NYU). Education: PhD in Computer Science, Princeton University Simons Society Junior Fellowship, Courant Institute (NYU) His work explores the theoretical foundations of data structure lower bounds, communication complexity, and secure computation. Recent research includes advancements in dynamic matrix inversion for linear programming, oblivious near-neighbor search, and the interplay between matrix rigidity and data structure efficiency. Key trends in his publications include: Proving polynomial and super-logarithmic lower bounds for static and dynamic data structures Developing novel techniques in information complexity and protocol compression Applications in parallel algorithms, compressed data structures, and algorithmic game theory Scientific Awards: NSF CAREER Award Simons Society Junior Fellow Best Paper Award at CSR '13 Advising and Grants: Advised PhD students Hengjie Zhang and Shunhua Jiang MsC student Victor Lecomte and postdoc Alexander Golovnev Research funded by NSF CAREER Award on data structure lower bounds Labs and Teams: Omri is affiliated with the Theoretical Computer Science Group at Columbia and leads the Data-Structure Lower Bounds Reading Group.

Arthur J. Lembo, Jr.View profile

Professor

• Geographic Information Systems (GIS)
• Geo-processing and spatial analysis
• Enterprise GIS implementation
• +7 more

Arthur J. Lembo, Jr. is Professor of Geography and Geoscience in the Henson School of Science and Technology at Salisbury University, where he has taught since 2007. He also serves as Technical Director of the Eastern Shore Regional GIS Cooperative, leading regional geospatial initiatives and enterprise GIS projects. Education B.S. Geography, State University of New York, College at Oneonta (1987) M.A. Geography, State University of New York at Buffalo (1989) Ph.D. Environmental Resource Engineering, State University of New York, College of Environmental Science and Forestry (1997) Research Interests Dr. Lembo’s research centers on the innovative application of advanced information technologies to geographic problem-solving. His work explores high-performance GIS through massively-parallel algorithms executed on consumer-grade gaming GPUs, enterprise-scale spatial database design, and the integration of smartphone sensors into real-time mapping workflows. Additional emphases include spatial decision support for post-disaster infrastructure assessment, crime analysis, and sustainable local food systems. Selected Research Sponsors National Science Foundation (multiple awards) W. K. Kellogg Foundation Maryland Governor’s Office of Crime Control and Prevention Maryland Department of Natural Resources USDA and NASA Scientific Awards 2018 State of Maryland Board of Regents Award for Teaching Excellence 2006 SUNY Chancellor’s Award for Excellence in Teaching 2005 NACTA Teacher of Merit 2003 CALS Faculty Innovation in Teaching Award, Cornell University 2002 & 2003 Master Teacher recognition, University of Mississippi & NASA Teaching & Outreach Dr. Lembo teaches core GIS programming and spatial-analysis courses (GEOG 204, 415, 419, 435, 619) and maintains an active extension program that provides GIS solutions for municipalities, first responders, and environmental NGOs such as The Nature Conservancy and the City of Havre de Grace. Lab & Teams He directs the Eastern Shore Regional GIS Cooperative, a university-based center that supports multi-agency geospatial data sharing and application development across Maryland’s Eastern Shore.

Michael CracraftView profile

Associate Professor

• Signal Integrity
• Power Integrity
• Electromagnetic Compatibility
• +3 more

Michael Cracraft is an Associate Professor in the Department of Electrical & Computer Engineering at Rose-Hulman Institute of Technology. He earned his PhD, MS, and BS from Missouri University of Science and Technology in 2007, 2002, and 2000 respectively. His work bridges theoretical and applied aspects of signal integrity, power integrity, and electromagnetic compatibility. PhD, Missouri University of Science and Technology (2007) MS, Missouri University of Science and Technology (2002) BS, Missouri University of Science and Technology (2000) Cracraft’s research focuses on critical challenges in high-speed electronic design, including: Signal Integrity (via placement, interconnects, differential pairs) Power Integrity (decoupling capacitors, PDN modeling, voltage ripple) Electromagnetic Compatibility (EMI reduction, EBG filters, common-mode suppression) RF Design (waveguides, fiber-optic systems, crosstalk mitigation) Recent publications highlight his contributions to physics-based modeling, particle-swarm optimization techniques, and 3D printed testing fixtures. His work spans both theoretical advancements (e.g., genetic via placement solvers) and practical hardware implementations (e.g., transient voltage detectors).

Keshav PingaliView profile

Professor

• Programming Languages
• Compiler Technology
• Parallel Computing
• +6 more

Keshav Pingali is a Professor and holds the W.A. 'Tex' Moncrief Chair of Grid and Distributed Computing in the Department of Computer Science at the University of Texas, Austin. He also holds a professorship at the Institute for Computational Engineering and Sciences at UT Austin. His educational background includes: B.Tech. from Indian Institute of Technology, Kanpur, India S.M. in Electrical Engineering from Massachusetts Institute of Technology ScD from Massachusetts Institute of Technology Dr. Pingali's research focuses on programming languages and compiler technology for program understanding, optimization, and parallelization. His current work centers on methodologies and tools for programming multicore processors, with particular emphasis on irregular applications from domains including graphics, social networks, and data mining. His research bridges the gap between theoretical computer science and practical high-performance computing systems, developing novel approaches to extract parallelism from complex applications. His publication record shows a consistent focus on parallel computing challenges, particularly in handling irregular applications that don't fit traditional parallel programming models. His work has evolved from foundational theoretical approaches to practical systems like Elixir that synthesize parallel graph programs, addressing the growing importance of graph-based computation in modern applications. His significant recognition includes: ACM SIGPLAN Programming Languages Achievement Award (2024) ACM/IEEE CS Ken Kennedy Award (2023) IEEE CS Charles Babbage Award (2023) Foreign Member of Academia Europaea (2020) Fellow of ACM, IEEE, and AAAS Dr. Pingali has advised PhD students including Lain Mustafaoglu, with whom he co-authored research on evolutionary policy optimization. His service to the academic community includes chairing the PPoPP steering committee (2003-2013), serving as program chair for PLDI 2014, and editorial roles for prestigious journals including ACM TOPLAS. He has received multiple teaching awards throughout his career and continues to teach advanced courses including 'Foundations of Machine Learning for Systems Researchers' in Fall 2025. His research group at the Institute for Computational Engineering and Sciences focuses on developing programming models and compiler technologies that enable efficient parallel execution of complex applications, particularly those with irregular structures that challenge conventional parallel programming approaches.

Vijaya RamachandranView profile

Professor

• Theoretical Computer Science
• Algorithm Design and Analysis
• Data Structures
• +3 more

Vijaya Ramachandran is the William Blakemore II Regents Professor in Computer Sciences at the University of Texas at Austin. Her research focuses on theoretical computer science, particularly algorithm design and analysis, data structures, graph theory, and parallel computation. Research Interests: Theoretical Computer Science Algorithm Design and Analysis Data Structures Graph Theory and Algorithms Parallel and Distributed Algorithms Effective Models for Computation Recent publications highlight advancements in distributed shortest path algorithms, sparse graph complexity, cache-oblivious data structures, and resource oblivious sorting. Her work explores foundational aspects of efficient computation across diverse models. Scientific Awards: Honorary Professor As a leading academic, she has mentored numerous students and participated in collaborative research projects, though specific details about her advisees and grants are not provided in this text.

Yangyang XuView profile

Associate Professor

• Stochastic Optimization
• Nonlinear Programming
• Computational Optimization
• +4 more

Yangyang Xu is an Associate Professor in the Department of Mathematical Sciences at Rensselaer Polytechnic Institute (RPI), where he conducts research at the intersection of optimization theory and computational methods. His work bridges theoretical foundations with practical applications in machine learning and data science. Dr. Xu's research focuses on several interconnected areas: Continuous optimization : specializing in stochastic programming, distributed optimization, and tensor optimization Optimization methods : developing stochastic approximation techniques, first-order methods, block update methods, and operator splitting approaches Computational frameworks : advancing parallel and distributed computing implementations for large-scale optimization problems Applications : applying optimization techniques to machine learning, signal processing, compressed sensing, and data mining challenges His recent publications (2023-2025) demonstrate a clear progression toward increasingly sophisticated optimization frameworks, particularly in decentralized and stochastic settings. Xu has made significant contributions to nonconvex optimization, minimax problems, and constrained optimization, with his work showing strong theoretical foundations coupled with practical implementations. Dr. Xu serves on the editorial board of Mathematics of Operations Research (since January 2023), reflecting his standing in the optimization community. He actively mentors six PhD students and has successfully guided multiple students to completion, with graduates moving to positions at organizations including Evonik, SAS, and UT Austin. His teaching includes graduate courses such as Stochastic Optimization and Reinforcement Learning and Introduction to Optimization. Dr. Xu leads a research group focused on advancing optimization theory while developing practical algorithms for real-world applications, particularly in distributed computing environments where communication efficiency is critical.

Martin MorenoView profile

Assistant Professor

• Compiler Optimizations
• High-Performance Computing
• Quantum Computing
• +3 more

Martin Moreno is a tenure-track Assistant Professor in the Department of Computer Science and Engineering at The Ohio State University. His office is located in Dreese Laboratories at 2015 Neil Ave, Columbus, OH. His research focuses on the intersection of compilers, programming languages, high-performance computing, and quantum computing, with goals to enhance program execution speed, reliability, and portability across architectures. Research Interests: Dr. Moreno specializes in polyhedral compilation, GPU optimizations, quantum computing abstractions, distributed-memory algorithms, and energy-aware computing. His work bridges theoretical compiler design with practical performance improvements for modern hardware, including GPUs and quantum processors. Recent Research Trends: Analysis of his 15 most recent publications reveals strong emphasis on: 1) Automated code generation for distributed systems and tensor computations, 2) Quantum program optimization through affine transformations, 3) Energy-efficient GPU computing via polyhedral models, and 4) Novel compiler frameworks for task-based parallelism. His work consistently integrates formal methods (e.g., SMT solvers) with practical runtime systems. Student Advising: Actively seeks motivated students for research projects in compiler optimizations and quantum computing. Applicants to OSU are encouraged to list him as a potential advisor and mention PLSE/HPC interests in statements of purpose.

Wei-Ming LinView profile

Professor

• Distributed and Parallel Computing
• Computer Architecture
• Computer Networks
• +6 more

Dr. Wei-Ming Lin is a Professor in the Department of Electrical and Computer Engineering at the University of Texas at San Antonio (UTSA) , where he has been since at least 2006. He previously served as the Associate Dean for Graduate Studies in the College of Engineering at UTSA from 2006 to 2015 and played a key role in developing four new graduate programs: M.S. in Advanced Manufacturing and Enterprise Engineering, Ph.D. in Mechanical Engineering, Master in Civil Engineering, and M.S. in Advanced Materials Engineering. His research spans Distributed and Parallel Computing , Computer Architecture , Computer Networks , Autonomous Control , and Internet Security . He has published extensively on SMT Processor Optimization , Resource Management , and High-Performance Computing , often integrating Machine Learning and Neural Networks into his work. Funding for his research has come from prestigious organizations such as the National Science Foundation (NSF) , Department of Defense (DOD) , Office of Naval Research (ONR) , and Air Force Office of Scientific Research (AFOSR) . The trends in his publications highlight a focus on Simultaneous Multi-Threading (SMT) Processors , Resource Allocation , and Energy Efficiency in computing systems. His work includes innovative approaches to Instruction Dispatching , Issue Queue Management , and Hashing Techniques for network applications. Dr. Lin has been involved in program committees for major international conferences and served as Program Chair for the International Conference on Computer Applications in Industry and Engineering (2004) . While no specific scientific awards are mentioned in the provided text, his extensive grant funding and leadership roles underscore his impact in the field.

Gillian Beltz-MohrmannView profile

Assistant Professor

• Cosmology
• Galaxy Formation
• Large-scale Structure
• +5 more

Gillian Beltz-Mohrmann serves as an Assistant Professor in the Department of Physics and the Program in Statistical and Data Sciences at Smith College. She recently completed a postdoctoral research fellowship in the Cosmological Physics and Advanced Computing Group at Argonne National Laboratory, bringing cutting-edge computational expertise to her academic position. Her academic credentials include: Ph.D. in Astrophysics from Vanderbilt University (2022) B.A. in Astrophysics from Wellesley College (2016) Professor Beltz-Mohrmann's research program sits at the critical intersection of large-scale structure, galaxy formation, and cosmology. She employs sophisticated computational approaches to investigate fundamental questions about dark matter and dark energy while advancing our understanding of galaxy evolution. Her methodology combines large-scale computer simulations with observational data to refine theoretical models of the universe's structure. She actively contributes to major cosmological initiatives as a member of both the Dark Energy Spectroscopic Instrument (DESI) Collaboration and the LSST Dark Energy Science Collaboration (DESC). Analysis of her publication record reveals a clear trajectory toward increasingly sophisticated computational methodologies in cosmology. Her recent work demonstrates significant integration of differentiable programming techniques with traditional astrophysical modeling, particularly evident in her 2024 paper on DiffOpt. The consistent focus across her publications centers on improving models of galaxy clustering, refining the galaxy-halo connection, and developing computational frameworks that bridge simulation and observation. Professor Beltz-Mohrmann maintains a strong commitment to inclusive practices within academia while actively participating in multiple research communities. Her scientific contributions extend beyond traditional publications to include meaningful engagement with open-source scientific software development, particularly through GitHub contributions to projects like DSPS (Differentiable Stellar Population Synthesis), Corrfunc, and halo_mass_correction. Her research program demonstrates integration across observational astronomy, theoretical cosmology, and advanced computational techniques, positioning her at the forefront of modern astrophysical research that leverages both traditional astronomical methods and contemporary data science approaches.

Zhao ChenView profile

Associate Professor

• Symbolic Computation
• Numerical Computation
• Matrix Computations
• +3 more

Dr. Zhao Chen serves as an Associate Professor in the Department of Mathematics within the School of Arts and Sciences at New York City College of Technology, City University of New York (CUNY). He holds a Ph.D. in Mathematics from the CUNY Graduate Center (2000) and teaches a comprehensive range of courses from developmental mathematics (MAT063) to advanced topics including cryptography research projects in upper-level classes. His scholarly work centers on symbolic and numerical computations, with deep specialization in structured matrix algorithms, displacement structures, and efficient polynomial root-finding methods. Chen's research bridges theoretical mathematics and practical computational applications, consistently focusing on accelerating solutions for linear systems and matrix operations through innovative preprocessing techniques and displacement-based frameworks. Analysis of his publication history (1998-2011) reveals sustained contributions to structured matrix theory, particularly Cauchy-like and Toeplitz systems. His work demonstrates progressive refinement from foundational displacement structures to practical algorithmic implementations, predominantly published in numerical analysis journals and high-performance computing conferences. Key themes include recursive factorization methods, operator theory applications, and polynomial division approximations. Chen actively contributes to educational infrastructure through co-development of campus-wide software systems: the MMT scheduling tool (with Alexander Rozenblyum), Advisement Registrations Schedule (with Rozenblyum, Sandie Han, and Henry Africk), and Random Generating Exams software (with multiple collaborators). These projects showcase his commitment to integrating computational research with institutional service while supporting student learning through applied cryptography projects.

Mayur NaikView profile

Professor

• Programming Languages
• Artificial Intelligence
• Neurosymbolic Programming
• +2 more

Mayur Naik is the Misra Family Professor in the Department of Computer and Information Science at the University of Pennsylvania. His research specializes in programming languages and artificial intelligence, with current focus areas including neurosymbolic programming, trustworthy AI for healthcare, and AI-enhanced development tools. He leads the development of the Scallop neurosymbolic programming language and maintains active collaborations in bioinformatics and clinical research. Education: Ph.D. in Computer Science, Stanford University (2008) M.S. in Computer Science, Purdue University (2003) B.S. from BITS Pilani (1999) Research Focus: His work bridges symbolic AI with neural approaches through neurosymbolic programming, enabling interpretable and domain-aware solutions. Current projects include Dolphin (scalable neurosymbolic learning), IRIS (LLM-assisted static analysis), and applications in computer vision and healthcare. Research emphasizes both theoretical foundations and practical system implementations. Publication Trends (Recent Focus): Recent work demonstrates strong emphasis on neurosymbolic learning frameworks (55%), AI-enabled programming tools (30%), and healthcare applications (15%). Key technical threads include LLM integration with program analysis, GPU acceleration for symbolic reasoning, and weakly-supervised learning paradigms. Awards & Honors: Test of Time Award, Foundations of SW Engineering (2023) Distinguished Paper Awards (PLDI 2019, SIGSOFT 2015, FSE 2015, SIGPLAN 2014) NSF CAREER Award (2013) Lockheed-Martin Teaching Excellence Award (2015) Outstanding Junior Faculty Award, Georgia Tech (2016) Academic Leadership: Advises 8 PhD students with research spanning neurosymbolic systems, program analysis, and AI security. Teaches core courses CIS 5470 (Software Analysis) and CIS 5500 (Database Systems). Research supported by NSF and industry partnerships. Labs & Teams: Leads the neurosymbolic programming research group at UPenn, collaborating with Intel Labs, Google AI, and healthcare institutions. Group develops open-source tools including Scallop compiler and LLM-assisted analysis frameworks.

Cody StevensView profile

Adjunct Professor

• GPU Computing
• Artificial Intelligence
• High Performance Computing (HPC) Education
• +2 more

Cody Stevens is an Adjunct Professor at Wake Forest University, supporting the Distributed Environment for Academic Computing (DEAC) supercomputing cluster as a High Performance Computing (HPC) systems administrator. He holds a Master of Science in Computer Science from Wake Forest University (2014), with a focus on NVIDIA GPU and CUDA-based parallel computing. Education: M.S. in Computer Science (2014) from Wake Forest University Research Interests: GPU Computing Artificial Intelligence HPC Education Parallel Computing Computational Biology Publication Trends: His research spans GPU-accelerated molecular dynamics simulations, graph theory applications in protein signaling, and algorithm development for coarse-grained models in bioinformatics. Key themes include computational biophysics and high-performance computing. Teaching & Advising: He teaches CSC 111 – Introduction to Computer Science and CSC 191 – Introduction to High Performance Computing , and advises Wake Forest University’s Student Cluster Competition team.