Yuri Bazilevs is the E. Paul Sorensen Professor of Engineering at Brown University's School of Engineering and Co-Director of the Mechanics of Undersea Science and Engineering Center. His research focuses on computational mechanics, isogeometric analysis, fluid-structure interaction, and high-performance computing. Prior to Brown, he held positions at UC San Diego, where he advanced to Full Professor in 2014 after a rapid tenure. He earned his PhD in 2006 and postdoc training in computational engineering at UT Austin's ICES. Research interests span computational fluid dynamics, solid mechanics, and advanced discretization methods like isogeometric analysis (IGA) and meshfree approaches. He has developed novel formulations for complex phenomena such as underwater explosions, composite material failure, and hypersonic flow dynamics. His work integrates cutting-edge numerical methods with practical engineering applications in aerospace, energy, and biomedical systems. Recent publications highlight advancements in IGA for architected materials, RKPM-based crack modeling, and stabilized formulations for compressible flows. His contributions bridge theoretical mechanics with computational innovation, addressing challenges in multiphysics coupling and large-scale simulations. Collaborations span academia and industry, emphasizing practical validation and real-world impact. Bazilevs' expertise includes variational multiscale methods, peridynamics for fracture mechanics, and immersive particle methods for fluid-structure interaction. His work has been applied to wind turbine aerodynamics, gas turbine optimization, and cardiovascular flow analysis. He actively contributes to computational infrastructure development, such as the tIGAr software framework for IGA automation.
Jiang Hu is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, holding the Eric D. Rubin '06 Endowed Professorship. He also serves as Co-Director of Graduate Programs and is affiliated with the Computer Science & Engineering department. His research focuses on VLSI design automation, machine learning applications, and hardware security. He has held roles as editor for IEEE Transactions on CAD and ACM Transactions on Design Automation, and chaired the 2012 ACM International Symposium on Physical Design. Education: B.S. in Optical Engineering (Zhejiang University, 1990), M.S. in Physics (1997), and Ph.D. in Electrical Engineering (University of Minnesota, 2001). He worked at IBM Microelectronics before joining Texas A&M in 2002. Research interests include energy-efficient VLSI circuits, on-chip communication fabrics, analog layout automation, and AI-driven EDA. Recent work emphasizes machine learning for design closure, privacy-preserving frameworks, and systolic array-based architectures. Awards: IEEE Fellow (2016) Humboldt Research Fellowship (2012) Multiple best paper awards at DAC, ICCAD, and ASPDAC Advising and grants: Leads initiatives like the SLICE project, NSF workshops on ML-EDA infrastructure, and serves as Editor-in-Chief of ACM TODAES since 2024. His work bridges academic research and industry applications in EDA and semiconductor design. Labs/Teams: Active contributor to open-source tools like ALIGN for analog layout generation and collaborations on machine learning for EDA commons.
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.
Yongjoo Park is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the Grainger College of Engineering. He leads research in data-intensive AI systems as a member of the Data and Information Systems (DAIS) lab, focusing on novel data systems that bridge database theory and practical AI applications. His work emphasizes open-source contributions through GitHub and direct societal impact. Research interests center on systems for data-intensive AI , particularly efficient Retrieval-Augmented Generation (RAG) systems for exploratory AI, data science versioning, and in-storage computing. Key projects include Kishu (the world's first undoable Jupyter notebook with time-travel capabilities), CARE (a causal-relational system for structured/unstructured data), and AirDB/AirIndex (serverless transactions and automatic index optimization). His group develops tools enabling scalable, optimized AI workflows from storage layers to LLM inference. Recent publications reveal a strong focus on interactive data systems (85% of recent work), with significant contributions to notebook environments (Kishu), vector databases (ISCA'25), and RAG optimization. Awards highlight technical innovation, including SIGMOD 2025 Best Demo Award and NSF CAREER funding. His open-source philosophy drives GitHub releases of all major systems. SIGMOD 2025 Best Demo Award (Kishu) NSF CAREER Award (Novel data science systems) SIGMOD'23 Best Artifact Award Honorable Mention (DeepOLA) IBM-Illinois Project Selection (VectorDB/RAG) Mentorship spans 12 current PhD/MS students and 6 graduated advisees, including Supawit Chockchowwat (now Postdoc at Google, future Assistant Professor at CMKL University). He teaches advanced courses like CS511 (Advanced Data Management) and recruits 1-2 new PhD students annually, prioritizing data systems research. His lab emphasizes diversity, individual respect, and concrete outcomes in a collaborative workspace.
Bryan Pardo is a Professor of Computer Science at Northwestern University and head of the Interactive Audio Lab. He co-directs the Northwestern Center for Human Computer Interaction + Design and chairs the Computer Science Diversity Committee. He teaches courses in Deep Learning, Machine Learning, Generative Modeling, and Digital Music Instrument Design. PhD in Computer Science and Engineering, University of Michigan MMus in Jazz and Improvisation, University of Michigan MS in Computer Science, Ohio State University BMus in Jazz Composition, Ohio State University His research focuses on machine understanding and manipulation of sound, particularly in music and speech domains. Key areas include Machine Learning (e.g., automated gradient clipping), Signal Processing (e.g., Multi-scale Common-fate Transform), and Human Computer Interaction. Applications involve inclusive audio interfaces, audio search engines, source separation, natural language-controlled audio effects, privacy-preserving adversarial attacks on voice recognition, and music co-creation tools. Recent publications highlight advancements in neural watermarking (MaskMark), masked acoustic modeling (VampNet), and real-time adversarial privacy systems for speech. His lab's work has been applied in Adobe's AI-powered audio editor and Lexie B2 hearing aids. Scientific Awards: $1.8 million NSF Future of Work award $440K NSF grant for accessible music programming $200K Toyota grant $100K Sony grant TorchCrepe pitch tracker: 20 million+ downloads Bryan Pardo advises PhD student Max Morrison and collaborates with researchers like Patrick O'Reilly, Zeyu Jin, and Prem Seetharaman. His lab develops technologies for blind and visually impaired audio creators, including HaptEQ and Eyes-free tools.
Simon Dobson is a Professor of Computer Science and Deputy Head of the School of Computer Science at the University of St Andrews. His research focuses on complex systems, sensor analytics, computational tools for simulation, and data analytics. He leads grants exceeding EUR30M, including a £5M EPSRC-funded programme in Sensor Systems Software. He is a Fellow of the Royal Society of Edinburgh (2020) and advises the Scottish government. Education: BSc (University of Newcastle), DPhil (University of York), both in Computer Science. Professional: Chartered Engineer, Fellow of the British Computer Society. Research Interests: Complex systems, network science, higher-order networks, epidemiological modeling, and sensor data integration. Teaching: CS4203 (Computer Security), CS5728 (Complex Systems Modelling). Supervises PhD/MSc projects. Awards: Includes RSE Fellowship, BCS Fellowship, and multiple leadership roles in conferences and committees.
Shiva Nejati is a Professor at the University of Ottawa 's School of Electrical Engineering and Computer Science . He holds a PhD in Computer Science from the University of Toronto and previously worked as a Senior Scientist (2012-2019) and Scientist (2009-2012) at the SnT Centre (University of Luxembourg) and Simula Research Laboratory. Research focus: Software engineering for cyber-physical systems (autonomous vehicles, IoT), blending formal verification, machine learning, and search-based testing Key tools developed: ARIsTEO, SOCRaTEs, SimCoTest, EPIcuRus Editorial roles: Associate Editor for EMSE Journal (2025–), ASE Journal (2025–), IEEE Transactions on Software Engineering (2020–2024) His work combines formal methods , empirical software engineering , and AI/ML to address verification challenges in complex systems, particularly through evolutionary algorithms and surrogate modeling . Notable collaborations include industry partners in telecommunications, automotive, and aerospace sectors. Recent publications emphasize large language models for requirements analysis, adversarial testing of vision systems, and multi-objective optimization for test generation. His Sedna Research Lab actively trains graduate students in these cutting-edge methodologies.
Michela Becchi is an Associate Professor in the Department of Electrical and Computer Engineering at North Carolina State University. She specializes in computer architecture, systems software, and applications, with a focus on heterogeneous systems, parallel algorithms, and acceleration techniques for bioinformatics, pattern recognition, and quantum computing. Her work spans multi-core CPUs, GPUs, FPGAs, and distributed clusters, emphasizing the boundary between hardware and software design. Dr. Becchi holds a Ph.D. and Master’s degree in Computer Engineering from Washington University in St. Louis (2009) and a Bachelor’s degree in Computer Engineering from Politecnico di Milano, Italy (2000). Her research has been recognized with prestigious awards, including the NSF CAREER Award (2015) and the University of Missouri System President Award for Early Career Excellence (2016). Her research interests include compiler and runtime techniques for heterogeneous systems, acceleration of bioinformatics algorithms, and high-speed networking applications. She has pioneered frameworks for efficient data transformation, GPU-accelerated compression, and memory-efficient graph algorithms for quantum computing. Her work also explores thread coarsening, mixed-precision auto-tuning, and secure multi-core processor design. Key contributions include the PILOT runtime system for GPU memory management, the GPU-FPtuner auto-tuner for floating-point applications, and innovative approaches to automata processors for genomic analysis. Her publications emphasize reproducible accuracy in scientific simulations and the optimization of irregular applications on many-core platforms.
Brandon M. Stewart is an Associate Professor of Sociology at Princeton University with extensive interdisciplinary affiliations. He serves as Director of the Statistics Core at the Office of Population Research and maintains formal connections with the Politics Department, Princeton Institute for Computational Science and Engineering, Center for Information Technology Policy, and Center for the Digital Humanities. Stewart holds editorial leadership as Co-Editor-in-Chief of Political Analysis and Associate Editor at Sociological Methods & Research . His educational background includes: Ph.D. in Government from Harvard University (2015) Master's degree in Statistics from Harvard University (2014) Stewart's research pioneers innovative quantitative methods for social science applications, specializing in automated text analysis and modeling complex heterogeneity in regression. His methodological frameworks enable researchers to uncover hidden structures in large datasets that were previously too costly or impossible to analyze. While his recent work has focused on using newspaper archives to study propaganda mechanisms in contemporary China, his tools are deliberately designed for broad applicability across diverse domains including education, human trafficking, forced migration, international relations, constitutional law, and psychology. His publication record demonstrates consistent innovation at the intersection of statistics, machine learning, and social inquiry. Stewart's work shows a clear trajectory from foundational methodological development to practical implementation across numerous substantive areas, with recurring themes of enhancing causal inference with textual data, developing robust topic modeling techniques, and creating accessible computational tools for social scientists. Stewart's scholarly excellence has been recognized through multiple prestigious awards: 2024 Leo Goodman (Early Career) Award from the Methodology Section of the American Sociological Association 2023 Emerging Scholar Award from the Political Methodology Society Edward R Chase Dissertation Prize Gosnell Prize for Excellence in Political Methodology Political Analysis Editor's Choice Award Recognition for Excellence in Mentoring Graduate Students As a mentor, Stewart has guided several successful graduate students to faculty positions at institutions including UCLA and Georgetown. His collaborative approach is evident in numerous multi-author projects spanning disciplines from political science to computational linguistics. His leadership extends to the Sociology Statistics Reading Group, which he founded to foster interdisciplinary methodological exchange, and his summer methods camp that trains social scientists in advanced quantitative techniques.
Zohreh Sharafi is an Assistant Professor of Software Engineering in the Department of Computer and Software Engineering (GIGL) at Polytechnique Montréal. Previously, she served as a Senior Research Fellow in the Department of Electrical and Computer Engineering at the University of Michigan, Ann Arbor, where she worked with Dr. Westley Weimer and was awarded the prestigious NSERC Postdoctoral Fellowship. Prior to her academic career, she worked as a software engineer at Morgan Stanley, contributing to the firm's electronic trading platform and serving as principal architect of SURF, a market data simulator. Her educational background includes a Ph.D. in Computer Engineering from École polytechnique de Montréal under the supervision of Dr. Giuliano Antoniol and Dr. Yann-Gaël Guéhéneuc, a Master of Applied Science in Software Engineering from Concordia University, and a Bachelor of Computer Engineering from the University of Tehran. Dr. Sharafi leads the SENSE Lab, a multidisciplinary software engineering research laboratory focused on understanding problem-solving strategies developers use during software development, with particular attention to human factors such as gender and native language. Her research combines human-centric design with experimental methodologies, investigating cognitive processes involved in software development using biometric measures including eye tracking and neuroimaging. Current active projects include evaluating trustworthiness perceptions of software artifacts and studying the role of creativity in software engineering tasks. She has made significant contributions to understanding how gender influences program comprehension and code review processes. Her publication record demonstrates a strong focus on empirical methods in software engineering, particularly eye tracking and neuroimaging techniques to study developer cognition. Her work spans program comprehension, code review, requirements engineering, and the impact of human factors on software development processes. She has developed methodological frameworks for conducting eye tracking studies in software engineering and has made notable contributions to understanding how visualization techniques affect software development tasks. NSERC Postdoctoral Fellowship NSERC Discovery Grant Program and Launch Supplements (Sep 2024-Sep 2029) IVADO Startup & Operation Fund (Jan 2022-Jan 2023) Scholarship for Doctoral Studies from Fonds de Recherche du Quebec Distinguished Reviewer Awards from IEEE ICPC 2020 and ACM FSE 2024 Dr. Sharafi actively mentors students including Mahta Amini (PhD Candidate, IVADO Scientifique en résidence 2024 Laureate), Cameron Cherif (PhD Candidate), Sara Yabesi (Master's Student), and Anthonia Njoku (Graduate research intern). She serves on numerous conference organizing committees including as Local Arrangement Chair for SANER 2025, Program Co-chair for SEMLA 2024, and as a reviewer for top-tier journals including IEEE Transactions on Software Engineering and ACM Computing Surveys. Her research is supported by multiple grants focused on understanding human factors in software engineering through empirical methods. At Polytechnique Montréal, Dr. Sharafi directs the SENSE Lab which brings together computer scientists, cognitive scientists, and software engineering researchers to investigate the cognitive aspects of software development. The lab employs advanced methodologies including eye tracking, functional near-infrared spectroscopy (fNIRS), and functional magnetic resonance imaging (fMRI) to study how developers comprehend, navigate, and modify software systems. Current projects examine trustworthiness perceptions in code review, the role of creativity in software engineering tasks, and gender differences in software development processes.
Carlo Alberto Furia is an Associate Professor and Vice Dean at the Faculty of Informatics, Università della Svizzera italiana (USI). He is affiliated with the Software Institute, where he leads the ATOM research group. His academic journey includes prior roles as an Associate Professor at Chalmers University of Technology and a Senior Researcher at ETH Zurich’s Chair of Software Engineering. PhD in Computer Science, Politecnico di Milano Master of Science in Computer Science, University of Illinois at Chicago Laurea in Computer Science and Engineering, Politecnico di Milano His research centers on formal methods for software engineering, aiming to enhance software correctness, reliability, and quality through rigorous techniques. Key areas include automated program verification, contract-based development, loop invariant inference, and empirical evaluation using Bayesian data analysis. He emphasizes practical applicability and automation in formal methods. His recent publications reflect a strong focus on program analysis at the bytecode level, multilingual software analysis, automated repair of Android security issues, and empirical methodologies. These works span topics such as JVM substitutability, exception behavior in Java bytecode, and information flow security, demonstrating a consistent thread in improving software robustness through formal and automated techniques. He is actively involved in the software engineering research community as an Associate Editor of the Empirical Software Engineering (EMSE) journal and as a Program Committee member for major conferences including FASE, FM, ASE, ICSE, and CauSE. Carlo Furia has advised multiple research projects and supervised student theses. He has led and contributed to funded research initiatives, particularly in program analysis and verification. His group has developed tools such as AutoProof and other software artifacts available through the ATOM software page. He regularly teaches courses such as Software Analysis, Programming Fundamentals, and Software Design & Modeling. He leads the ATOM research group, which focuses on advancing automated techniques for software testing, analysis, and verification. The group develops practical tools and conducts empirical studies to validate research outcomes.
Ralf Bierig joined Maynooth University's Computer Science Department in 2017, teaching topics including information retrieval, software testing, interaction design, and virtual reality. He is the programme director of the Higher Diploma in Human-Computer Interaction (HCI) and User Experience (UX). He earned his BSc (2002) from University of Furtwangen and PhD (2008) from Robert Gordon University. Research Interests His work spans information retrieval, interactive information retrieval, personalisation, information search behavior, usability (UX), and virtual reality (VR). Recent publications focus on multimodal concept indexing, hybrid IR approaches, and contextual adaptation in search systems. Publication Trends His research combines statistical semantics, graph modeling, and multimodal data analysis across academic collaborations in Austria, Germany, and international venues like ECIR and SIGIR.
Vivek Srikumar is an Associate Professor in the Kahlert School of Computing at the University of Utah, co-leading the Utah NLP group and affiliated with the Utah Center for Data Science. His research focuses on Machine Learning and Natural Language Processing, particularly in structured prediction, bias mitigation, and healthcare NLP applications. He teaches Machine Learning (CS 6350/DS 4350) and has been supported by NSF, NIH, and corporate grants from Intel, Google, and others. Education: Ph.D. in Computer Science, University of Illinois at Urbana-Champaign (2013) Postdoctoral Researcher at Stanford University's NLP Group (2013-2014) Visiting Researcher at Allen Institute for Artificial Intelligence (2022 sabbatical) Research Interests: Srikumar explores text understanding, structured learning, and robust AI systems. His work addresses challenges in table-based reasoning, adversarial robustness, and ethical AI. He develops methods to ensure models use appropriate evidence and mitigate biases in representations. Grants & Collaborations: Supported by NSF, NIH, BSF, and industry partnerships with Intel, Google, Verisk, Bloomberg, and Nvidia. Notable projects include table QA systems (TempTabQA), bias mitigation (OSCaR/VERB), and crisis counseling NLP tools (ClientBot). Advising: Supervised over 30 students, including 15+ Ph.D./M.S. alumni now in academia and industry (e.g., Google, Amazon, Microsoft). Current advisees focus on multimodal reasoning, healthcare NLP, and AI ethics. Labs/Teams: Utah NLP Group and Utah Center for Data Science. Active in reproducibility efforts (LogFlux) and open-source tools (CogCompNLP/Pylon frameworks).
Austin Rovinski is an Assistant Professor in the Department of Electrical and Computer Engineering at New York University’s Tandon School of Engineering. He specializes in chip design, electronic design automation (EDA), and open-source hardware methodologies. His research focuses on VLSI design, domain-specific accelerators, and chiplet-based systems. Prior to NYU, he held a postdoctoral position at Cornell University and earned all his degrees (Ph.D., M.S., and B.S.) from the University of Michigan. Education: Ph.D., Electrical Engineering, University of Michigan - Ann Arbor Master’s, Electrical Engineering, University of Michigan - Ann Arbor Bachelor’s, Electrical Engineering, University of Michigan - Ann Arbor Research Focus: Developing open-source EDA frameworks like OpenROAD Optoelectronic interconnect systems for 2.5D packaging Agile hardware design methodologies Reconfigurable sparse matrix accelerators RISC-V-based manycore processors (e.g., Celerity project) Key Contributions: Austin led the development of the OpenROAD RTL-to-GDS flow and contributed to the Sirius and Celerity projects. His work emphasizes reproducibility, democratizing chip design through open-source tools. Awards: IEEE Micro Top Picks (2015) Michigan EECS Outstanding Research Award (2016) NSF Graduate Research Fellowship Honorable Mention (2017, 2018) Advising & Grants: Actively mentors graduate students in chip design and EDA. His research is supported by NYU’s Tandon School of Engineering and collaborations with industry partners. Labs & Teams: Core contributor to the OpenROAD project, part of NYU’s hardware design and EDA initiatives, and collaborator on the Celerity manycore processor project.
Ion Stoica is a Professor in the Electrical Engineering and Computer Sciences Department at the University of California, Berkeley, where he holds the Xu Bao Chancellor Chair. He serves as Director of the Sky Computing Lab and is Executive Chairman of both Databricks and Anyscale. His research spans distributed systems, cloud computing, and AI systems, with significant contributions to large-scale data processing frameworks. Stoica's research interests focus on the intersection of AI and systems, with emphasis on developing practical implementations that bridge theoretical foundations with real-world deployability. His work addresses fundamental challenges in distributed computing, resource management, and large-scale machine learning systems. Current projects include Ray (a distributed execution framework), vLLM (a high-throughput inference engine for LLMs), Chatbot Arena (an open platform for human preference evaluations), and SkyPilot (a framework for running AI workloads across clouds). His research output demonstrates a consistent trajectory toward more efficient, scalable systems for modern AI workloads, particularly focusing on optimizing inference performance, resource utilization, and cross-cloud deployment. Recent publications reflect growing interest in large language model serving, video generation optimization, and agent-based systems. ACM Fellow SIGOPS Hall of Fame Award (2015) SIGCOMM Test of Time Award (2011) ACM Doctoral Dissertation Award (2001) Member of National Academy of Engineering Honorary Member of the Romanian Academy Stoica has advised an extensive number of doctoral students who have gone on to prominent positions in academia and industry, including assistant professorships at Stanford, MIT, Carnegie Mellon, and other top institutions. He has received significant research funding through his lab activities and startup ventures. His research group has been particularly successful in translating academic research into widely adopted open-source technologies and commercial products. Stoica leads the Sky Computing Lab at UC Berkeley, which focuses on developing systems for AI workloads across multiple clouds. His research group has produced numerous influential open-source projects including Apache Spark, Apache Mesos, and Alluxio, which have become industry standards for large-scale data processing. The lab maintains strong industry partnerships while pursuing fundamental research in distributed systems and AI infrastructure.
