Quanquan Liu is an Assistant Professor of Computer Science at Yale University. His research focuses on algorithms for large data, dynamic and distributed graph algorithms, parallel computing, differential privacy, and Byzantine-resilient systems. He holds a PhD in Computer Science from MIT's Theory Group and has held postdoctoral positions at Northwestern University and MIT. Education: PhD in Computer Science, MIT (Advisors: Erik Demaine and Julian Shun) MEng in Computer Science, MIT B.S. in Computer Science and Math, MIT (Advisor: David Karger) Research Interests: Theory and practice of algorithms for large-scale data, dynamic/distributed graph algorithms, parallel and high-performance computing, differential privacy, and Byzantine-resilient algorithms. Recent Highlights: His work includes practical differentially private graph algorithms, efficient parallel algorithms for graph problems, and fair course allocation mechanisms. Notably, he received the Best Paper Award at SPAA 2022 for parallel dynamic graph algorithms. Service: PC member for PPoPP, ESA, SPAA, and ALENEX Coach for USA Computing Olympiad (USACO) and Northwestern's ICPC team Current Group: Advising PhD students Felix Zhou and Pranay Mundra, and Master's student Jinghua Sun.
Chris Chipot is a Researcher and former Research Director at the CNRS, affiliated with the University of Lorraine. He holds adjunct faculty positions at the University of Illinois (Department of Physics) and the University of Chicago (Department of Biochemistry and Molecular Biology). He directs an Associate International Laboratory between CNRS and the University of Illinois at Urbana-Champaign. His expertise spans theoretical chemistry, molecular dynamics simulations, and membrane protein studies. Education: PhD in Theoretical Chemistry (1994, Henri Poincaré University, France) with a fellowship from Roussel Uclaf Institute. Habilitation (2000, University of Lorraine). Research Interests: Focuses on protein-ligand binding free energies, membrane protein dynamics, computational methods for molecular simulations, and biophysical techniques. His work integrates theoretical approaches with experimental validations, particularly in drug design and membrane biology. Publications: Recent articles highlight advancements in free-energy calculations, membrane protein dynamics, and scalable molecular dynamics software (NAMD). His research bridges computational and experimental biophysics, addressing challenges in structural biology and drug discovery. Awards: Fellowship from the Roussel Uclaf Institute during PhD studies. Advising & Grants: Leads collaborative projects via the CNRS-UIUC lab, fostering international research partnerships. His work has been supported by institutional and international grants, though specific funding details are not detailed here. Labs/Teams: Directs the CNRS-UIUC Associate International Laboratory, promoting interdisciplinary research in computational biophysics and membrane protein studies.
Ugur Cetintemel is the Khosrowshahi University Professor of Computer Science at Brown University, where he has been since completing his PhD at the University of Maryland in 2001. His research focuses on data management systems, database systems, distributed systems, and stream processing, with recent work integrating AI techniques into database systems. He teaches courses such as Database Management Systems and Data Science fundamentals. Notable contributions include the Aurora and Borealis stream processing engines, S-Store for transaction processing, and DBPal for natural language interfaces. His work emphasizes scalable, efficient systems for large-scale data challenges. Education: PhD in Computer Science, University of Maryland, 2001 MS in Computer Science, Bilkent University, 1996 BS in Computer Science, Bilkent University, 1994 Research Interests: Data management, stream processing, distributed systems, predictive analytics, and AI integration with databases. Key projects include optimizing database systems for modern hardware, developing real-time stream processing frameworks, and exploring interactive data exploration techniques. Grants & Advising: Extensive contributions to grants and collaborations, though specific grant details are not listed. Supervises graduate students in areas like database systems and machine learning integration. Part of the Brown Data Management Group. Labs/Teams: Leads research within the Brown Data Management Group, focusing on advancing database systems for big data and real-time analytics.
Tamás Budavári is an Associate Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University (JHU), with joint appointments in Physics and Astronomy and a secondary appointment in Computer Science. He is affiliated with the Whiting School of Engineering and the Institute for Data-Intensive Engineering and Science (IDIES). His research focuses on computational and statistical methods for big data in astronomy and interdisciplinary applications such as urban blight analysis. Education: PhD in Astrophysics (2001), Eötvös Loránd University, Budapest Master’s in Theoretical Physics (1997), Eötvös Loránd University Research Interests: Budavári develops algorithms for handling large astronomical datasets, including Bayesian inference, streaming algorithms, and GPU-accelerated processing. His work includes SkyQuery (an online astronomy data tool), photometric redshift estimation, and cross-matching catalogs. He also applies computational methods to urban planning, such as optimizing strategies to address vacant housing in Baltimore City. Publications & Tools: Budavári’s recent work spans topics like deep learning for astronomical image restoration, combinatorial optimization for urban policy, and probabilistic catalog matching. His tools, such as CUDAHM and NWAY, enable scalable analysis of multi-epoch survey data and N-way catalog cross-identification. Awards & Grants: Recipient of the Gordon and Betty Moore Fellowship and SAMSI Research Fellowship Funded by NSF, STScI, NIH, and others Leadership & Outreach: He serves on the Steering Committee of the 21st Centuries Cities Initiative and is a founding editor of the Journal of Astronomy and Computing. His interdisciplinary work bridges astrophysics, data science, and urban systems.
Guglielmo Scovazzi is a Professor at Duke University with appointments across multiple departments including the Department of Civil and Environmental Engineering, the Thomas Lord Department of Mechanical Engineering and Materials Science, and as Professor of Mathematics. His interdisciplinary research bridges computational mechanics, scientific computing, and engineering applications. Dr. Scovazzi earned his B.S/M.S. in aerospace engineering (summa cum laude) from Politecnico di Torino (Italy), followed by an M.S. and Ph.D. in mechanical engineering from Stanford University. Prior to joining Duke, he was a Senior Member of the Technical Staff at Sandia National Laboratories' Computer Science Research Institute. His research focuses on developing advanced numerical methods for computational mechanics, particularly finite element methods for fluid and solid mechanics. Key areas include multiphase porous media flows, computational methods for materials under extreme conditions, turbulent flow computations, and instability phenomena. His work emphasizes creating accurate computational approaches that reduce design/analysis costs for complex engineering problems involving fluid-structure interactions and transient phenomena in complex geometries. Dr. Scovazzi's most significant recent contribution is the development of the Shifted Boundary Method, an innovative computational framework that enables efficient simulations on complex geometries without requiring boundary-fitted meshes. This method has found applications in geomechanics, energy systems, and resilient infrastructure design. Kavli Fellow, National Academy of Sciences & Kavli Foundation (2018) Presidential Early Career Award for Scientists and Engineers (PECASE), White House (2017) Early Career Award, U.S. Department of Energy, Advanced Scientific Computing Research Program (2014) Dr. Scovazzi teaches multiple courses in computational mechanics including Nonlinear Finite Element Analysis and Introduction to the Finite Element Method. His research has been supported by substantial federal funding, and he actively collaborates across disciplines to address challenging problems in energy, environment, and infrastructure resilience through advanced computational methods.
Jee Choi is an Assistant Professor in the Department of Computer and Information Science within the College of Arts and Sciences at the University of Oregon. His research focuses on developing high-performance algorithms for big data analytics, particularly in tensor decomposition and parallel computing systems. Education: PhD in Electrical and Computer Engineering, Georgia Institute of Technology, 2015 MS in Electrical and Computer Engineering, Georgia Institute of Technology, 2004 BS in Electrical and Computer Engineering, Georgia Institute of Technology, 2000 Research Interests: Dr. Choi specializes in High Performance Computing with expertise in parallel algorithm design, performance modeling, and energy efficiency. His work targets Tensor Decomposition & Data Mining for big data, developing scalable solutions for sparse and dense tensor computations. He advocates leveraging HPC systems to transform massive datasets into societal benefits through efficient data mining techniques. Publication Trends: Choi's recent publications (2021-2025) demonstrate consistent innovation in tensor decomposition methodologies, with increasing focus on adaptive storage (Alto), streaming data factorization, and energy-aware autotuning. His work spans GPU clusters, distributed systems, and emerging architectures, maintaining strong connections to real-world big data challenges while advancing theoretical algorithm design. Professional Background: After completing his PhD under Richard Vuduc at Georgia Tech (notable for SpMV GPU autotuning and Energy Roofline Model), Choi conducted DoD-funded research on tensor decomposition at IBM Watson Research Center (2015-2018) before joining the University of Oregon faculty.
Erik Waingarten is an assistant professor at the University of Pennsylvania in the Computer and Information Science department. His research focuses on algorithms for massive datasets, including similarity search, streaming/sketching, property testing, and distribution testing. Former postdoctoral researcher at Stanford's CS Department under Moses Charikar PhD from Columbia University advised by Xi Chen and Rocco Servedio Key research areas: High-dimensional geometry Streaming algorithms Property testing Sketching techniques Clustering and metric optimization Recent article trends show expertise in: 2025 publications on monotonicity testing and metric property analysis 2024 work on Earth Mover's Distance and kernel evaluations 2023 papers on clustering, optimal transport, and MST algorithms 2022-2020 foundations in sublinear algorithms and entropy estimation Scientific recognition: NSF CAREER Award (2023) CCC Best Paper Award (2017) Invited to Journal of the ACM (2017) Academic advising includes PhD students: Ashwin Padaki Tian Zhang Nicolas Menand Krish Singal Junkai Song
Kyle C. Hale is an Associate Professor at Oregon State University's School of Electrical Engineering and Computer Science (College of Engineering). He holds a Ph.D. and M.S. from Northwestern University (2016, 2013) and a B.S. in Computer Science from UT Austin (2010). Prior to joining Oregon State in 2024, he served as an Associate Professor at Illinois Tech in Chicago. His research spans operating systems, high-performance computing (HPC), virtualization, computer architecture, and system security. Current work focuses on specialized system software stacks for emerging computing paradigms like memory disaggregation and parallelism optimization. He leads the HExSA Lab and collaborates with the HiPCastor group. Scientific Awards: NSF CAREER Award (2023-2028) Illinois Tech College of Computing Excellence in Research (2023) Illinois Tech College of Computing Excellence in Teaching (2021) Illinois Tech Department of Computer Science Teacher of the Year (2020) EuroSys '22 Best Artifact Award Recent Research Trends: His publications emphasize compiler techniques for memory-disaggregated systems, optimizing parallel runtimes through hardware-software integration, virtualization at fine granularities, and accelerating machine learning workloads via system-level innovations. Keywords include HPC, virtualization, parallelism, and secure execution contexts. Teaching: Courses taught include Computer Architecture (CS/ECE 472), System Security (CSP 544), Operating Systems (CS 450), and advanced topics in serverless/edge computing. He actively recruits PhD students to the HExSA Lab.
Scott Kerlin is a Senior Lecturer in the Department of Electrical Engineering and Computer Science at Oregon State University's College of Engineering. He holds an M.S. and B.S. in Computer Science from the University of North Dakota. Prior to academia, he worked at the Mayo Clinic on medical software systems and IBM as a build master for enterprise products. His career spans roles including network administrator, lab manager, and Undergraduate Director at UND. Dr. Kerlin's research focuses on bridging industry experience with academic curriculum, particularly in computer science education, project-based learning, and cybersecurity. He emphasizes practical applications of theoretical concepts, such as integrating 3D printing and scanning technologies with security systems for small satellites. His work also explores student efficacy in AI courses and scalable software project management methodologies. He has taught at multiple institutions, including the University of Minnesota and Augsburg University, and held roles at Michigan Tech as Senior Security Engineer. His 2024 Engineering+ Outstanding Teaching Award highlights his commitment to pedagogical innovation. Current projects include in-space 3D printing, solar energy systems, and cryptographic solutions for satellite communications. Key areas of contribution include: 3D printing/Scanning: Material characterization, key replication, and aerospace applications Cybersecurity: Intrusion detection, satellite communications security, and chaotic cryptosystems Educational Innovation: Active learning frameworks, PBL implementation, and student performance modeling His interdisciplinary approach connects computer science fundamentals with real-world engineering challenges, emphasizing sustainability and industry relevance in curricula.
Brandon Reagen is an Assistant Professor in the Department of Electrical and Computer Engineering at New York University's Tandon School of Engineering, with affiliations in Computer Science, the Center for Advanced Technology in Telecommunications (CATT), and the NYU Center for Cybersecurity (CCS). He holds a PhD in Computer Science from Harvard (2018) and undergraduate degrees in Computer Systems Engineering and Applied Mathematics from the University of Massachusetts, Amherst (2012). His research focuses on computer architecture, hardware acceleration for deep learning and privacy-preserving computation, and VLSI design. He pioneered efficient deep learning accelerator designs through unsafe optimizations and contributed to benchmarking frameworks like Aladdin and MachSuite. His work spans privacy-preserving machine learning, secure computing systems, and hardware-software co-design for cryptographic protocols. Key achievements include the NSF CAREER Award (2024) and Siebel Scholar recognition (2018). His research centers on advancing secure computing through innovations like zero-knowledge proof accelerators (e.g., zkSpeed), fully homomorphic encryption frameworks (Orion), and entropy-guided privacy techniques for large language models. He leads interdisciplinary efforts at CATT and CCS to bridge hardware design and cybersecurity challenges. Reagen's contributions include over 50 publications in top-tier conferences (e.g., ISCA, ASPLOS, MLSys) and industry collaborations at Facebook AI. His work emphasizes practical solutions for encrypted computation efficiency, privacy-preserving inference, and scalable secure systems.
Shantanu Dutt is a full Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Chicago , located in Chicago, IL. His office is in 930 SEO at 851 S. Morgan St, and he can be reached at dutt@uic.edu . Education Ph.D. in Computer Science and Engineering, University of Michigan, Ann Arbor (1990) M.Tech. in Computer Engineering, Indian Institute of Technology Kharagpur (1984) B.E. in Electronics and Communication Engineering, Maharaja Sayajirao University of Baroda (1983) Research Interests Professor Dutt’s research agenda centers on VLSI Computer-Aided Design (CAD) , with particular emphasis on physical design and incremental synthesis targeting both ASICs and FPGAs. He explores discrete optimization techniques to solve placement, routing, and partitioning problems. Another major thrust is FPGA built-in self-test (BIST) and trusted design , ensuring provable diagnosability and security against hardware Trojans. He also investigates fault-tolerant computing at both chip and system levels, and develops parallel and distributed computing algorithms for scalable performance. Scientific Awards 1996 Best Paper Award , ACM/IEEE Design Automation Conference, for “A probability-based approach to VLSI circuit partitioning” 1995 Most Influential Paper Award , Fault-Tolerant Computing Symposium, for “Design and reconfiguration strategies for near-optimal k-fault-tolerant tree architectures” Research Impact and Funding Professor Dutt has published extensively in top-tier journals (IEEE TVLSI, ACM TRETS, IEEE TCAD) and premier conferences (ICCAD, DAC, DATE, FTCS). His work has shaped incremental placement, timing-driven routing, FPGA security, and fault-tolerant multiprocessor architectures. While specific grant numbers are not listed, the breadth and longevity of his publication record indicate sustained funding from NSF, industry, and other agencies. Laboratory and Collaborations Although no formal laboratory name is provided, Professor Dutt’s research is conducted within the ECE department’s VLSI CAD and Fault-Tolerance groups, collaborating with graduate students and colleagues across the US and internationally.
Mehmet Esat Belviranli is an Assistant Professor in the Computer Science Department at the Colorado School of Mines, where he directs the High Performance Systems and Software Lab (HyperSys). His research focuses on increasing resource utilization in heterogeneous architectures through runtime systems, scheduling algorithms, and performance modeling, with publications in top venues including MICRO, PPoPP, and SC. Education: Ph.D. in Computer Science, University of California, Riverside (2016) M.S. in Computer Science, Bilkent University (2009) B.S. in Computer Science, Bilkent University (2006) Belviranli's research spans heterogeneous architectures, runtime systems, performance modeling, parallel programming, autonomous computing, deep learning acceleration, cyber-physical systems, and edge-cloud platforms. His work develops analytical models and programming abstractions to address resource management, scheduling, and security challenges in diversely heterogeneous systems, with applications in edge computing, autonomous systems, and machine learning acceleration. Recent projects emphasize real-world constraints and security implications. His publication trends reveal increasing focus on edge-cloud resource management (e.g., HARNESS), security vulnerabilities in heterogeneous systems (e.g., MC3), and deep learning acceleration under resource constraints. Key themes include memory contention modeling, scheduling for cyber-physical systems, and concurrent DNN execution, reflecting a shift toward practical deployment in security-sensitive edge environments. Scientific Awards: U.S. Air Force Research Lab Summer Faculty Fellowship Award (2022) U.S. Air Force Research Lab Summer Faculty Fellowship Award (2021) Oak Ridge National Laboratory Significant Event Award (2019) Best Paper Finalist, IEEE HPEC 2018 Outstanding Paper Award, DATE 2024 Belviranli mentors Ph.D. students Ismet Dagli (MLCommons Rising Star 2024, CGO'24 SRC finalist) and Justin Davis (DATE'24 Outstanding Paper Award winner). He has secured $2M+ in funding from NSF, DoE, and SRC, including an NSF-SaTC grant on mobile security (2024), a DoE grant on superconductive systems (2023), and an NSF FuSe grant on graphene nanoribbons (2023), often leading multi-institutional teams from Rochester, Virginia, Arizona, and Minnesota. The HyperSys Lab develops ecosystems for high-performance heterogeneous systems, with recent projects including HARNESS for edge-cloud resource management and MC3 for mobile SoC security. The lab has received equipment donations from Google Coral.ai and Xilinx, and collaborates with national labs on security challenges and next-generation semiconductor technologies.
Dr. Jing Li is an Associate Professor and Eduardo D. Glandt Faculty Fellow at the University of Pennsylvania , holding dual appointments in the Electrical and Systems Engineering and Computer and Information Science departments. As co-director of the CyberSavvy nationwide security research center and director of the Penn Computational Intelligence Lab (PennCIL) , she pioneers innovations in non-von Neumann computing paradigms. Her research spans post-CMOS technologies, in-memory computing, and hardware-software co-design for security and AI applications. PhD in Computer Engineering, Purdue University (2009) BSc in Electrical Engineering, Shanghai Jiaotong University (2004) Research Focus: Dr. Li's work addresses fundamental challenges in computer systems across the stack. Key areas include: In-Memory Computing: Liquid Silicon architecture combining RRAM with silicon CMOS through monolithic 3D integration Security Engineering: Transforming computer security from "Art" to formal "Engineering" discipline within CyberSavvy Virtualization: Cloud FPGA abstraction layers decoupling compilation from runtime resource management Graph Analytics: Degree-aware optimization techniques for massive-scale graph processing Deep Learning Systems: Roofline model extensions for FPGA-based CNN acceleration Scientific Impact: Awarded DARPA Young Faculty Award , NSF CAREER Award , and IBM CEO Milestone Award , her team has achieved world records in energy-efficient computing (ENIAD supercomputer). With 46 U.S. patents and over 80 publications, she leads ecosystem development for emerging computing architectures through initiatives like the open-source MEG simulation platform . Community Leadership: Dr. Li serves on program committees for flagship conferences ( ISCA , FPGA Symposium ), chairs the International Memory Workshop , and contributes to the MLsys conference's inaugural committee. She actively mentors through multiple PhD openings and industry collaborations.
Jason Cong is the Volgenau Chair for Engineering Excellence and Distinguished Chancellor's Professor in the Computer Science Department at UCLA's Samueli School of Engineering. He directs the Center for Domain-Specific Computing (CDSC) and the VLSI Architecture, Synthesis, and Technology (VAST) Laboratory, and serves as Associate Vice Provost for Internationalization and Co-Director of UCLA/PKU Student and Scholar Program. Dr. Cong's research spans electronic design automation, customizable computing for machine learning and big-data applications, quantum computing, and highly scalable algorithms. His work has produced over 500 publications with more than 41,000 citations and an H-index of 106. His recent work focuses on quantum computing compilation, domain-specific acceleration for AI workloads, and high-level synthesis optimization techniques that leverage machine learning. His publication trend shows a strong emphasis on quantum computing and machine learning acceleration in recent years, with numerous papers on quantum layout synthesis, LLM acceleration, and high-performance FPGA implementations. His team has developed frameworks like TAPA for task-parallel dataflow programming and RapidStream for automated parallel implementation of FPGA designs. Member of National Academy of Engineering (2017) IEEE Robert N. Noyce Medal recipient (2022) Phil Kaufman Award recipient (2024) ACM Chuck Thacker Breakthrough Award recipient (2024) 18 Best Paper Awards across major conferences Multiple 10-Year Retrospective Most Influential Paper Awards Dr. Cong has graduated 50 PhD students, many of whom are now faculty at major research universities or hold key positions at leading tech companies. He has led over 100 research projects funded by DARPA, NSF, SRC, and industry sponsors. His entrepreneurial activities include founding three successful companies (Aplus Design Technologies, AutoESL, and Falcon Computing Solutions), all acquired by major EDA players. His VAST Laboratory continues to push boundaries in domain-specific computing, with active research in quantum computing, AI acceleration, and high-performance FPGA implementations.
Gene Cooperman is a Professor at the Khoury College of Computer Sciences at Northeastern University, with an affiliation in the College of Engineering. His research focuses on high-performance computing (HPC), transparent checkpoint-restart systems, and model checking. He leads the High Performance Computing Laboratory, where he explores checkpointing technologies like DMTCP, MANA for MPI, and CRAC for CUDA, aiming to enhance HPC workflows on supercomputers such as NERSC's Perlmutter. His work bridges distributed computing, parallel algorithms, and system software to address challenges in fault tolerance, scalability, and resource management. Cooperman has advised 10 PhD students and co-authored over 125 refereed publications, contributing to projects like Geant4-MultiThreaded and Roomy for disk-based computation. His teaching includes courses on computer systems and HPC seminars. Education: Background in computational algebra and parallel computing, transitioning to HPC systems and checkpointing. Research Themes: Transparent checkpointing, MPI agnostic solutions, CUDA integration, and HPC resource optimization. Recent articles emphasize MPI checkpointing, reversible debugging (FReD), and CUDA support, reflecting trends in distributed and GPU-accelerated systems. His grants include NSF, NERSC/DOE, and MemVerge funding. Cooperman collaborates with institutions like CERN and NERSC, advancing applications in particle physics simulations and supercomputing. Current students include Aayushi Gautam, Jiajun Cao, Rohan Garg, and Twinkle Jain.
