Xiaolei Fang is Associate Professor in the Edward P. Fitts Department of Industrial and Systems Engineering at North Carolina State University. His research develops advanced statistical learning, deep learning, and optimization methods for industrial applications involving high-dimensional data, with particular focus on condition monitoring, failure prognostics, and system performance optimization. He holds a PhD in Industrial Engineering and MS in Statistics from Georgia Tech. Professor Fang's research integrates machine learning with industrial engineering to solve complex problems in predictive maintenance, quality control, and energy systems. His methodological innovations include federated learning approaches for privacy-preserving prognostics, distributionally robust machine learning models, and tensor-based statistical methods for manufacturing quality diagnostics. He has received multiple prestigious awards including the ISE Outstanding Research Award (2024), Sigma Xi Best PhD Thesis Award (2019), and SAS Data Mining Best Paper Award (2016). His research has been funded by NSF, Cisco Systems, and the US Department of Energy. Professor Fang teaches courses in Quality Design & Control, Statistical Models for Systems Analytics, High-Dimensional Data Analytics, and Optimization Models. He has supervised 9 PhD students to completion and currently advises 7 graduate students working on projects spanning federated learning for prognostics, tensor-based quality control, and machine learning applications in manufacturing and energy systems.
Anshumali Shrivastava is an Associate Professor of Computer Science, Electrical and Computer Engineering, and Statistics at Rice University, affiliated with the George R. Brown School of Engineering. His research focuses on large-scale machine learning, randomized algorithms for big data, and graph mining. He holds a PhD from Cornell University (2015) and an MSc from the Indian Institute of Technology Kharagpur (2008). His research interests span scalable deep learning, efficient neural network inference, and probabilistic algorithms. He has pioneered techniques in compressed learning, hashing-based search, and distributed optimization for handling massive datasets. Notable contributions include methods for accelerating LLM inference, memory-efficient quantization, and graph processing algorithms. Teaching: Probabilistic Algorithms, Large-Scale ML, and Machine Learning Seminars Awards: Charles W. Duncan Jr. Achievement Award (2023), Young Faculty Research Award (2021), NSF CAREER Award (2017), and multiple best paper awards His work bridges algorithm design with practical applications in recommendation systems, genomics, and edge computing. Current efforts focus on sustainable AI, hardware-aware compression, and efficient training/inference pipelines for large models.
Professor Reynold Cheng is a faculty member at the University of Hong Kong (HKU), specifically within the Department of Computer Science in the School of Computing and Data Science (CDS). He currently serves as the Division Head of the AI & Data Science Division at CDS and is part of the Steering Committee of the Musketers Foundation Institute of Data Science. His academic journey includes a BEng and MPhil from HKU (1998–2000) and an MSc and PhD from Purdue University (2003–2005). Prior to HKU, he was an Assistant Professor at the Hong Kong Polytechnic University (HKPU) from 2005 to 2008. Cheng’s research focuses on data science, big graph analytics, and uncertain data management. He has received numerous awards, including the SIGMOD Research Highlights Reward 2020, HKICT Awards 2021, and HKU Knowledge Exchange Award (Engineering) 2021. His work has been recognized through grants such as the HKU-TCL Joint Research Centre for AI-funded project (HKD 1M, 2020–2022) and a CRF-funded project for real-time monitoring of infectious diseases (HKD 6.5M, 2021–2022). Cheng actively contributes to academic service, including serving as PC co-chair for IEEE ICDE 2021 and editorial roles in journals like IS and DAPD. His publications span top venues like SIGMOD, VLDB, and KDD, emphasizing algorithm design for large graphs and probabilistic data systems.
Olof Bälter is a Professor in Computer Science at KTH Royal Institute of Technology, affiliated with the Division of Media Technology and Interaction Design within the School of Electrical Engineering and Computer Science. He is the founder of the Technology-Enhanced Learning research group and holds a focus on learning engineering and human-computer interaction. His research interests center on technology-enhanced learning , question-based learning , learning analytics , AI in education , and inclusive pedagogy . A consistent theme in his work is improving efficiency in education and daily life through digital tools. He developed the Pure Question-Based Learning (Pure QBL) methodology, a digital Socratic approach that enhances student engagement and learning outcomes. His work extends to wellness in education through initiatives like walking seminars, and he investigates digital interventions for mental health, such as online Cognitive Behavioral Therapy (CBT) courses. His recent publications highlight trends in AI-generated educational content , learning efficiency , digital pedagogy , and inclusive course design , with applications in computer science education, language instruction, and global development. His research often employs experimental and data-driven methods, including randomized controlled trials and learning analytics. Teacher of the Year at the Surveying program KTH's Pedagogical Prize Higher Education Hero STINT Excellence in Teaching Scholarship (2008 and 2013) Olof Bälter has supervised numerous courses in programming, computer science, media technology, and learning engineering. He has collaborated with institutions such as Stanford University, Williams College, Region Stockholm, Stockholm University, and organizations like Promobilia and Begripsam. His projects aim to scale effective learning methods globally and make education more accessible and efficient. He leads research on the effectiveness of Pure QBL for students with ADHD and is involved in developing digital tools for health literacy and professional development in Ethiopia and Rwanda. His work bridges theory and practice, aiming to transform educational delivery through innovation and evidence-based design.
Sherry Tongshuang Wu is an Assistant Professor at Carnegie Mellon University's School of Computer Science, with primary appointments in the Human-Computer Interaction Institute (HCII) and secondary affiliation with the Language Technology Institute (LTI) . Trained at the University of Washington under Jeffrey Heer and Dan Weld, she bridges HCI and NLP to study human interactions with AI systems across diverse user groups. Her educational background includes a Ph.D. (2016-22) and M.S. (2016-18) in Computer Science and Engineering from the University of Washington, and a B.Eng. (2012-16) from Hong Kong University of Science and Technology. Industry experience includes research internships at Google Brain, Microsoft Research, and Apple. Wu's research focuses on three interconnected pillars: Real-world AI Evaluation (developing frameworks like SPHERE for systematic assessment), Task-specific AI Test & Distill (optimizing general-purpose models for specific use cases), and Human-AI Task Delegation (designing optimal collaboration between humans and AI). Her work emphasizes practical deployment, user-specific net gains, and error recovery mechanisms. Analysis of her 15 most recent publications reveals a strong trend toward evaluation frameworks (35%), human-AI collaboration systems (30%), and specialized model distillation (25%), with growing emphasis on educational applications (10%). Key methodological themes include checklist-based evaluation, perspective-aware retrieval, and structural analysis of AI outputs. Google Academic Research Award (2024) Amazon Research Awards (2024) AIED 2024 Best Paper Award ACL 2020 Best Paper Award Rising Stars in EECS Workshop (2020) Wu actively mentors 19 students across PhD, Master's, and undergraduate levels, with notable projects including synthetic data generation, LLM literacy tools, and retrieval system optimization. She leads significant grant-funded work through Amazon Research Awards and Google Academic Research Awards, focusing on deployable model generation and human-AI collaboration frameworks. Her lab develops practical tools like Promp2Model and SPHERE that bridge theoretical research with industry applications.
M. Tamer Özsu is a University Professor of Computer Science at the David R. Cheriton School of Computer Science, University of Waterloo, where he holds a Cheriton Faculty Fellowship. He also serves as a Distinguished Visiting Professor at Tsinghua University and is the Founding Director of Waterloo-Huawei Joint Innovation Laboratory since 2018. His extensive contributions to computing have earned him numerous prestigious awards including the 2024 ACM Presidential Award for long-standing and significant contributions to the computing field. Professor Özsu's research focuses on data engineering aspects of data science, particularly addressing data management issues with two main foci: management of non-traditional data and large-scale distributed data management. He is renowned for his seminal book "Principles of Distributed Database Systems" (co-authored with Patrick Valduriez), now in its fourth edition, and the "Encyclopedia of Database Systems" (co-edited with Ling Liu), in its second edition. His work bridges theoretical foundations with practical system implementations, targeting grand societal challenges through computational approaches. His recent publications reveal a strong trend toward graph analytics, streaming data processing, and the integration of large language models with vector data management. The research shows increasing focus on GPU-accelerated graph processing, RDF query optimization, and multimodal data analysis, reflecting the evolution of data management challenges in the era of big data and AI. His work continues to address fundamental challenges in distributed data systems while adapting to emerging technologies and application domains. Scientific Awards and Fellowships ACM Presidential Award (2024) IEEE TCDE Education Award (2024) IEEE Innovation in Societal Infrastructure Award (2022) CS Can | Info Can Lifetime Achievement Award (2018/2019) ACM SIGMOD Test-of-Time Award (2015) ACM SIGMOD Contributions Award (2006) The Ohio State University College of Engineering Distinguished Alumnus Award (2008) Fellow of the Royal Society of Canada Fellow of the American Association for the Advancement of Science (AAAS) Life Fellow of the Association for Computing Machinery (ACM) Life Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA) Elected member of the Science Academy, Türkiye Professor Özsu has been deeply involved in academic leadership and community building. As Founding Editor-in-Chief of ACM Books (2013-2019), he launched a series that by 2019 had published 28 major books with another 30 under contract. His service to ACM, particularly through SIGMOD, has been exemplary and widely recognized. He directs the Waterloo-Huawei Joint Innovation Laboratory, which focuses on cutting-edge research in data management and distributed systems, fostering strong industry-academia collaboration.
Istvan Albert is a Research Professor of Bioinformatics at Pennsylvania State University , affiliated with the Department of Biochemistry and Molecular Biology . He leads the Bioinformatics Consulting Center and teaches BMMB 852: Applied Bioinformatics . Research Interests: Specializing in bioinformatics, large-scale biological data analysis, microarray and sequence analysis, scientific programming, algorithm development, and database-driven web development. His work spans gene ontology visualization , RNA-Seq analysis , and coronavirus research . Software Development: Created GeneScape for gene function visualization and bio for bioinformatics workflows. Maintains the Biostar Handbook series and the Biostars Q&A Forum , a leading bioinformatics resource.
Ravid Shwartz-Ziv is an Assistant Professor and Faculty Fellow at NYU's Center for Data Science, with a dual role as Senior Research Scientist at Wand AI. His research bridges theoretical foundations and practical applications in artificial intelligence, focusing on Large Language Models (LLMs), information theory, and neural network interpretability. Ph.D. in Computational Neuroscience, Hebrew University of Jerusalem (2021) B.Sc. in Computer Science and Computational Biology, Hebrew University of Jerusalem (2014) His research spans: Developing min-p sampling for LLM text generation Preventing representation collapse in Transformers Creating contamination-free LLM benchmarks like LiveBench Advancing information-theoretic frameworks for neural networks Exploring representation learning and model adaptation Recent publications demonstrate expertise in LLM efficiency, self-supervised learning, and multi-agent systems. Notable awards include the Google PhD Fellowship, Moore-Sloan Fellowship, and multiple best paper recognitions. He has led research initiatives at Intel and Google AI, focusing on neural network compression, XGBoost comparisons for tabular data, and innovative benchmarking frameworks.
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.
Dr. Xin Zhou is an Oxford-Bristol Myers Squibb Fellow at the Department of Computer Science, University of Oxford. Her research integrates computational modeling, clinical data, and experimental findings to investigate cardiac disease mechanisms and develop human-based simulations for drug evaluation. BSc and MSc in Life Sciences, Beijing Normal University DPhil in Computational Biology, University of Oxford Her work focuses on multi-scale cardiac modeling , particularly in ischemic heart disease and heart failure, exploring ionic currents, tissue conduction, and organ-level dynamics. She develops electromechanical simulations to study cardiac alternans and arrhythmic risks, translating these into clinical applications for patient stratification and pharmaceutical testing. Recent publications emphasize in silico clinical trials , sex-specific cardiometabolic analysis, and Purkinje network modeling. Collaborative efforts with clinicians and pharmaceutical partners highlight her translational approach to regulatory science. Model of the Year 2024, BioModels EPSRC Impact Acceleration Account Microsoft Research Project Award Recognition Award, University of Oxford She supervises PhD and MSc students in computational cardiology, while serving on the editorial board of Frontiers in Physiology . Her current projects involve digital twinning and predictive cardiac safety models to reduce animal testing reliance.
Cristian Danescu-Niculescu-Mizil is an Associate Professor at the Department of Information Science , Cornell University . His research focuses on computational frameworks for social behavior analysis through large-scale natural language data , particularly in online communities and crisis counseling contexts. Recipient of WWW Best Paper Award (2013) and Yahoo! Key Scientific Challenges Award Co-developer of ConvoKit , a comprehensive conversational analysis toolkit Principal investigator in NLP for mental health , antisocial behavior detection , and linguistic coordination studies His work spans computational social science and human-centered AI , with recent publications in EMNLP 2025 , ACL 2025 , and CSCW 2025 focusing on conversation dynamics modeling , pivotal moment detection , and talk-time equity analysis . He has advised multiple PhD students who became faculty at Harvey Mudd , University of Michigan , and Thompson Reuters . Key scientific contributions : 2013 : WWW Best Paper - Power dynamics through linguistic coordination 2016 : IJCAI NLP & Journalism Best Paper - Gender bias quantification 2017 : ICWSM Best Paper - Trolling behavior analysis He teaches NLP and Social Interaction (INFO/CS 6742) and Language and Information (INFO/CS 4300) , with teaching resources including the Politeness Web App and ConvoWizard interactive tool featured in NPR's Weekend Edition . His work has been covered by New Scientist , NBC Today Show , and New York Times .
Oisin Mac Aodha is a Reader (Associate Professor) in Machine Learning at the School of Informatics, University of Edinburgh. He is also an ELLIS Scholar and founder of the Turing interest group on biodiversity monitoring and forecasting, having previously served as a Turing Fellow from 2021-2025. Mac Aodha completed his undergraduate degree in electronic engineering from the University of Galway in Ireland, followed by his MSc and PhD at University College London (UCL). His academic journey includes postdoctoral positions at UCL (2013-2016) working with Prof. Gabriel Brostow and Prof. Kate Jones, and at Caltech (2016-2019) in Prof. Pietro Perona's Computational Vision Lab as part of the Visipedia team. His research centers on computer vision and machine learning with emphasis on 3D understanding, human-in-the-loop methods, and AI for conservation and biodiversity monitoring. He has made significant contributions to monocular depth estimation (including the influential Monodepth2 paper), fine-grained visual categorization, and biodiversity monitoring systems. His work bridges theoretical machine learning with practical ecological applications, developing tools for species identification, range estimation, and conservation efforts. Recent publications reveal a strong trend toward ecological applications while maintaining fundamental contributions to 3D vision and representation learning. His major scientific achievements include: Turing Fellow (2021-2025) ELLIS Scholar Founder of the Turing interest group on biodiversity monitoring and forecasting Co-organizer of the Fine-Grained Visual Categorization (FGVC) workshop series at major vision conferences Mac Aodha advises multiple PhD students and postdocs working on computer vision for biodiversity monitoring, 3D understanding, and human-in-the-loop learning. His team has developed practical tools like Whombat (an open-source annotation tool for bioacoustics) and contributed to field-deployed biodiversity monitoring systems. He has served as Area Chair for top conferences including NeurIPS, CVPR, ICCV, and ICML, demonstrating his standing in the computer vision community. His research group collaborates extensively with ecologists at University College London, particularly with Prof. Kate Jones' team, bridging machine learning expertise with ecological domain knowledge. The Vision at Edinburgh group he contributes to focuses on developing practical AI tools that address real-world conservation challenges while advancing fundamental computer vision research.
James S. Kim is a Professor of Education at Harvard University's Graduate School of Education, where he conducts policy-relevant research focused on improving literacy outcomes for low-income students and struggling readers. With an Ed.D. from Harvard University (2002), he leads the READS Lab (Research Enhances Adaptations Designed for Scale in Literacy), a research team that partners with school districts to solve literacy challenges through evidence-based interventions. Dr. Kim's research centers on understanding how building students' domain knowledge and reading engagement can foster long-term improvements in reading comprehension. His work emphasizes experimental design and evidence-based interventions, with a particular focus on addressing educational inequality. His research interests include early education, education policy, evidence-based intervention, human development, inequality and education gaps, informal and out-of-school learning, language and literacy development, and teachers and teaching. Kim's most significant contribution is the Model of Reading Engagement (MORE), a spiraled and sustained content literacy intervention co-developed with schoolteachers that has been shown to improve first to third-grade students' reading comprehension in science, English language arts, and math. Notably, research on MORE meets WWC (What Works Clearinghouse) standards without reservation, and long-term follow-up suggests the intervention's impact persists through fourth grade. His publications reveal a consistent focus on content literacy, domain knowledge development, and transfer effects in reading comprehension. Research on MORE meets WWC standards without reservation Long-term effects of MORE persist through fourth grade Commitment to Open Science principles (open data, open materials, preregistration) As a servant leader, Kim builds long-term partnerships with school districts to implement literacy interventions at scale. His READS Lab promotes open science practices while developing practical solutions to literacy challenges. His research on summer reading interventions, parental text messaging, and classroom-based content literacy approaches demonstrates his commitment to translating research into practice. Kim's current work includes scaling the MORE intervention to improve reading comprehension for high-needs students in moderate to high poverty schools through a Department of Education-funded project (2024-2028).
Karthik Menon serves as an Assistant Professor with a joint appointment in the Woodruff School at Georgia Institute of Technology and the Coulter Department of Biomedical Engineering. His research integrates fluid mechanics, computational modeling, and data-driven methodologies to address critical challenges in healthcare, renewable energy, and bio-inspired engineering systems. His academic credentials include: Ph.D. in Mechanical Engineering, Johns Hopkins University (2021) M.S. in Mechanical Engineering, Johns Hopkins University (2019) B.E. in Mechanical Engineering, Birla Institute of Technology and Science, Pilani, India (2015) Menon's research program centers on three interconnected domains: cardiovascular flows for personalized treatment of heart disease, fluid-structure interactions in biological systems like heart valves and bio-mimetic robots, and vortex-dominated flows for renewable energy applications. His approach combines high-fidelity computational modeling with machine learning to uncover fundamental physics and develop clinical solutions, such as cardiovascular digital twins for non-invasive risk assessment. Current projects focus on patient-specific hemodynamics using CT imaging and uncertainty quantification to improve surgical planning. Analysis of his 15 most recent publications (2023-2025) reveals a dominant focus on advancing multi-fidelity computational frameworks for cardiovascular applications. Key trends include Bayesian uncertainty quantification, zero-dimensional solver development, and integration of clinical imaging data to create predictive digital twins. His work bridges fluid dynamics with clinical cardiology, targeting improved outcomes in coronary artery disease and Kawasaki-related complications through physics-informed machine learning. Menon's scholarly contributions have been recognized through competitive awards: WCCM-PANACM 2024 Travel Award, U.S. Association for Computational Mechanics (2024) Future Faculty Symposium Travel Award, Society of Engineering Science Conference (2023) Mark O. Robbins Prize in High-performance Computing, Johns Hopkins University (2021) Corrsin-Kovasznay Outstanding Paper Award, Johns Hopkins University (2020) Prosperetti Travel Award, Johns Hopkins University (2017) Mechanical Engineering Departmental Fellowship, Johns Hopkins University (2016) As principal investigator of the ComBiNE Fluid Dynamics Lab, Menon mentors graduate students in developing computational tools for fluid-structure interaction problems. His collaborative projects with cardiologists at Stanford and Emory hospitals translate engineering principles into clinical applications for cardiovascular disease management. Current grant activities focus on NSF and NIH-funded initiatives for uncertainty-aware cardiovascular modeling and bio-inspired flow energy harvesting. The ComBiNE Fluid Dynamics Lab operates as an interdisciplinary hub where engineers, clinicians, and data scientists collaborate on fluid mechanics challenges. Current lab initiatives include developing real-time hemodynamic simulators for surgical planning, creating reduced-order models for cardiac device optimization, and investigating vortex dynamics in fish schooling for underwater vehicle design. The lab maintains strong partnerships with Children's Healthcare of Atlanta and the Parker H. Petit Institute for Bioengineering and Bioscience.
Carey E. Priebe is a Professor in the Department of Applied Mathematics and Statistics at the Whiting School of Engineering, Johns Hopkins University. He maintains strong affiliations with multiple research centers including the Johns Hopkins University Center for Imaging Science, the Mathematical Institute for Data Science, and the Human Language Technology Center of Excellence. His academic career spans several decades with significant contributions to statistical methodology and theory. Dr. Priebe's research focuses on computational statistics, statistical pattern recognition, and statistical inference for high-dimensional and graph data. His work bridges theoretical statistics with practical applications in areas such as brain connectome mapping, network analysis, and image processing. He has made significant contributions to spectral graph theory, graph matching, and vertex nomination, with applications ranging from neuroscience to national security. His publication record demonstrates consistent contributions to statistical methodology, with a notable emphasis on graph-based statistical methods. His research trajectory shows increasing focus on network data analysis, particularly in the last decade, with applications to brain mapping and connectome analysis as evidenced by his NSF BRAIN Initiative grant and Nature publication. 2013 Erskine Fellow (University of Canterbury) 2011 McDonald Award for Excellence in Mentoring and Advising 2010 ASA SDNS Distinguished Achievement Award 2009 Erskine Fellow (University of Canterbury) 2008 National Security Science and Engineering Faculty Fellow 2008 Pond Award for Excellence in Teaching NSF BRAIN EAGER grant recipient (2014) Professor Priebe has supervised an extensive number of doctoral students whose work spans statistical methodology, network analysis, and machine learning. His students have secured positions at prestigious institutions including academia (University of Wisconsin, Boston University), government research labs, and major technology companies (Microsoft, Facebook, Amazon). His research has been supported by significant grants from NSF, DARPA, and other agencies focused on national security applications and fundamental statistical methodology development. He maintains active collaborations across multiple disciplines and institutions, as evidenced by his numerous conference presentations and visiting appointments including at The Alan Turing Institute and The Isaac Newton Institute. His work bridges theoretical statistics with practical applications in neuroscience, security, and data science.
