Dr. Oliver Kennedy is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo's School of Engineering and Applied Sciences. He serves as Co-Director of Graduate Studies and leads the Online Data Interactions (ODIn) Lab. His research focuses on databases, programming languages, and user interfaces for data science, with particular emphasis on scalable compilers and managing uncertainty in data. Kennedy holds a PhD in Computer Science from Cornell University (2011), MS from Cornell (2008), and dual BS degrees in Computer Science and Computer Engineering from NYU and Stevens Institute of Technology (2005). His work bridges theoretical computer science with practical data management challenges. His recent publications demonstrate a strong focus on improving database query processing, uncertainty management in data systems, and developing practical tools for data integration and exploration. Awarded the NSF CAREER Award in 2018, Kennedy's research has significant implications for efficient data processing in scientific and commercial applications.
Andrew Bishara, MD, is an Assistant Professor in Residence in the Department of Anesthesiology within the School of Medicine at the University of California, San Francisco (UCSF). He is affiliated with multiple UCSF clinical sites, including Mission Bay, Mount Zion, and Parnassus, and is actively involved in the AI Clinical Innovation Lab, Transplant Anesthesia Research Group, and POCCO (PeriOperative Cardiac Complications Observatory). His clinical practice as an anesthesiologist is deeply integrated with his research in machine learning and artificial intelligence for perioperative care. Dr. Bishara's educational background includes a BSE in Mechanical Engineering from MIT (2009), an MD from Harvard Medical School (2014), and a D.ABA. in Anesthesiology from UCSF (2019). He also completed specialized training in Medical Informatics and Artificial Intelligence through the Bakar Computational Health Sciences Institute (2020) and a Diversity, Equity, and Inclusion Champion program at UCSF (2022). His research focuses on developing and validating machine learning models to predict and prevent surgical complications such as acute kidney injury, postoperative delirium, pain, and blood loss in real time. He emphasizes creating clinically usable models and improving AI-human interfaces for seamless integration into clinical workflows. His work also explores gender-based disparities in coronary artery disease diagnosis using EHR data analytics. His recent publications demonstrate expertise in AI quality improvement, model implementation in acute care, and predictive modeling across diverse surgical and critical care domains. He co-founded Bezel Health, a company focused on healthcare quality measurement, reflecting his commitment to translating research into real-world impact. Clinical Artificial Intelligence Quality Improvement Real-time Risk Assessment in Surgery AI Integration in Anesthesia Gender Disparities in Cardiac Care Transplant Anesthesia Research Regulatory Aspects of AI in Medicine Dr. Bishara is actively engaged in advancing perioperative medicine through innovation in data science and AI, with a strong emphasis on improving patient outcomes, equity, and clinical workflow efficiency.
H. V. Jagadish serves as the Edgar F Codd Distinguished University Professor and Bernard A Galler Collegiate Professor of Electrical Engineering and Computer Science at the University of Michigan's College of Engineering. He directs the Michigan Institute for Data Science (MIDAS) and leads the NSF-funded Framework for Integrative Data Equity Systems (FIDES), following a career that included heading AT&T Labs' Database Research Department prior to joining Michigan in 1999. His academic foundation includes a Ph.D. from Stanford University, with prior appointments at AT&T Labs and the University of Illinois. Research spans database systems, data management, and data science, emphasizing data integration across heterogeneous sources and usability for non-technical users through schema design, natural language querying, and analytics with missing data. Current work focuses on equity, ethics, and fairness in AI, developing methods to identify and mitigate data inequities while ensuring societal benefits of data science. His white papers and National Academies presentations have shaped big data research agendas. Major honors include: Fellow of the ACM (2003) Fellow of the AAAS (2018) ACM SIGMOD Contributions Award (2013) David E Liddle Research Excellence Award (2008) Stephen S. Attwood Award Distinguished Faculty Award from the University of Michigan (2019) He has secured multiple NSF grants (IIS 0741620, IIS 1017296, IIS 1250880, 1741022, 1934565) supporting database usability and data equity research, mentored numerous graduate students, and created the widely adopted Data Science Ethics MOOC available on EdX, Coursera, and FutureLearn. His h-index of 96 reflects 200+ major publications and 38 patents. As MIDAS Director, he oversees university-wide data science initiatives while leading the EECS database research group and formerly directing the Software Systems Laboratory. His FIDES institute remains central to developing equitable data systems, with ongoing work expanding into AI policy frameworks.
Dr. YANG Guomin is an Associate Professor of Computer Science at Singapore Management University's School of Computing and Information Systems, where he coordinates the BSc Cybersecurity Track. His research focuses on privacy-preserving cryptography, authentication systems, and secure IoT frameworks. Research spans cryptographic protocols for cloud security, blockchain applications, and federated learning with emphases on efficiency and practical implementation. Recent publications demonstrate innovations in threshold authentication, redactable blockchains, and privacy-aware communication protocols. Advisees include LI Huilin and WANG Jiaheng, with research projects examining hardware-enhanced encryption, biometric authentication policies, and space network security. Work consistently addresses tension between security guarantees and computational efficiency in distributed systems.
Randy Harris is a Professor in the Department of English Language and Literature at the University of Waterloo, with a cross-appointment to the David Cheriton School of Computer Science. His career spans over three decades, focusing on the intersection of rhetoric , cognitive science , and computational linguistics . He holds a PhD in Communication and Rhetoric from Rensselaer Polytechnic Institute, with postdoctoral experience at the University of Alberta. Education : PhD (Rensselaer), MSc (Rensselaer, Alberta), MA (Dalhousie), BA (Queen's) Research Interests : Cognitive Rhetoric, Computational Rhetoric, Rhetorical Figures, Construction Grammar, Rhetoric of Science, and Neurocognitive Stylistics. His work explores how rhetorical figures like chiasmus and antimetabole reflect brain structure and cognitive processing. Recent projects involve building the Rhetoricon , a collocational database of rhetorical figures, and investigating their role in large language models . He has received numerous awards, including the University of Waterloo Arts Award for Excellence in Research (2023) and Fellow of the Royal Society of Canada (2022) . Harris has supervised graduate students in professional communication , linguistics , and science writing . His grants include NSERC Discovery Horizon (2024-2029) and multiple SSHRC grants for Computational Rhetoric and Rhetorical Figure Ontology . He actively organizes interdisciplinary workshops like Computing Figures and contributes to media discussions on language, AI, and rhetoric.
Charith Mendis is an Assistant Professor in the Siebel School of Computing and Data Science at the University of Illinois at Urbana-Champaign, with joint appointments in the Department of Computer Science, Electrical and Computer Engineering, and the Coordinated Science Lab. His research focuses on the intersection of compilers, program optimization, and machine learning systems. Dr. Mendis received his educational background from prestigious institutions: Ph.D. in Computer Science from Massachusetts Institute of Technology (2020) S.M. in Computer Science from Massachusetts Institute of Technology (2015) B.Sc. in Electronics and Telecommunication Engineering from University of Moratuwa (2013) His primary research interests center around compiler technology and machine learning systems. Mendis leads the ADAPT lab at UIUC, where his team works on creating high-performance ML optimization techniques and automated compiler construction using machine learning and formal methods. His work bridges the gap between traditional compiler design and modern machine learning approaches, with applications in tensor compilers, graph neural networks, and sparse computation. He has developed novel frameworks for optimizing deep learning workloads, verification of compiler transformations, and performance modeling for emerging hardware architectures. Mendis has established himself as a leading researcher in compiler optimization for machine learning systems, with a particular focus on tensor compilers, graph neural networks, and performance modeling. His recent publications demonstrate increasing sophistication in combining formal methods with machine learning techniques to solve challenging problems in compiler optimization and verification, with multiple papers accepted at top-tier conferences including OOPSLA, PLDI, POPL, and SIGMOD. His notable scientific achievements include: Google ML and Systems Junior Faculty Award (2025) DARPA Young Faculty Award (2024) NSF CAREER Award (2024) Distinguished Paper Award at POPL (2025) William A. Martin Thesis Award for Outstanding SM thesis, MIT (2015) Multiple teaching excellence awards at UIUC (2021-2023) Dr. Mendis actively mentors students through the ADAPT lab, offering research opportunities for undergraduates, master's students, and PhD candidates interested in compiler technology and machine learning systems. His research is supported by significant funding from the ACE center (part of JUMP 2.0), National Science Foundation (NSF), DARPA, IIDAI, and industry partners including Google, Intel, Amazon, and Qualcomm. He teaches advanced courses in compiler construction and machine learning for compilers. He leads the ADAPT lab at UIUC, which focuses on developing advanced compiler technologies for modern machine learning workloads. The lab maintains active collaborations with industry partners and has established itself as a leading research group in compiler optimization for AI systems. Current projects include tensor compilers, graph neural network optimization, and automated verification of deep learning systems.
Ronald J. Glotzbach is an Associate Professor at Purdue University's West Lafayette campus within the School of Applied and Creative Computing . His work focuses on web programming and development , leading projects that integrate dynamic content, databases, and educational technologies . He has taught courses such as CGT 356 (Web Programming), CGT 353 (Interactive Media), and CGT 456 (Advanced Web Programming). B.S. in Computer Graphics Technology M.S. in Technology Ph.D. in Curriculum and Instruction (pursuing) His research explores leading-edge web technologies for delivering interactive content, emphasizing web-enabling software, dynamic media integration, and mobile programming . Key trends in his publications include RSS technologies in education , web-based evaluation systems , and geospatial data tools for environmental sciences. Scientific awards include: Outstanding Professor (2003, 2004) CGT Dwyer Award for Outstanding Undergraduate Teaching (2005) CGT Outstanding Un-Tenured Faculty Award (2008) Professor Glotzbach has led numerous student teams in CGT projects , served as SIGGRAPH Student Volunteer Chair (2003-2005) , and collaborated with industry partners like Boeing (F-15 Distributed Mission Trainer) and Microsoft (XML Documents Testing Team) . He also provided expert testimony in a copyright case (2006) for Wargo & French, LLP.
Weipeng Zhou is a Postdoctoral Associate at the Yale School of Medicine within the Department of Biomedical Informatics and Data Science . Working under Professor Hua Xu , he specializes in pre-training and evaluating large medical language models using electronic health records and medical claims data. His work bridges Natural Language Processing , Biomedical Data Science , and Clinical Informatics to address critical healthcare challenges. PhD in Medical Informatics from the University of Washington (2025) Bachelor's in Computer Science and Statistics from the University of Wisconsin (2019) His research involves NLP/LLM applications in healthcare domains such as: Long COVID characterization and prediction Cardiovascular disease analysis Suicide prevention through clinical text mining Clinical note section identification Emerging water contaminant detection via PubMed article analysis His publications focus on model transferability , automated cohort discovery , and contextual health research tools . Notable collaborations include work with teams at University of Washington and Yale .
Michael J. Franklin is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley's College of Engineering. He has a prolific publication record spanning over three decades with more than 300 publications in top-tier database and systems conferences and journals, demonstrating his continued active research and leadership in the field. Franklin's research spans multiple areas within data management, with a recent focus on time-series analysis, AI-integrated database systems, cloud-native databases, and data quality. His work has evolved from traditional database systems to address modern challenges in big data, machine learning integration, and distributed systems. He has made significant contributions to data cleaning, crowdsourced data management, and stream processing systems. Analysis of his recent publications (2022-2025) reveals a strong trend toward integrating AI/ML capabilities with database systems, particularly in time-series anomaly detection, LLM applications for data management, and resource-adaptive query processing for cloud environments. His work increasingly focuses on practical systems that address real-world data challenges, often involving collaborations with industry partners and other leading academic researchers. Throughout his career, Franklin has mentored numerous PhD students who have become prominent researchers in their own right, including Sanjay Krishnan, Aaron Elmore, and Jiannan Wang. His collaborative research has frequently involved significant funding from NSF and industry partnerships, enabling large-scale systems research with real-world impact. Franklin leads research efforts that bridge theoretical database principles with practical system implementations. His work on projects like Data Station demonstrates his commitment to building trustworthy infrastructure for data sharing and analysis, addressing critical challenges in data privacy, security, and usability in collaborative environments.
Johes Bater is an Assistant Professor of Computer Science at Tufts University's School of Engineering. He joined Tufts in 2022 after postdoctoral research at Duke University's Database Group and a Ph.D. in Computer Science at Northwestern University. Education B.Sc. in Electrical Engineering (2011) from Stanford University M.Sc. in Electrical Engineering; Computer Systems (2013) from Stanford University Ph.D. in Computer Science (2020) from Northwestern University Research Focus Bater's research focuses on privacy-preserving analytics , balancing security , privacy , and utility in trustworthy database systems . His work spans differential privacy , secure computation , and data federation to enable robust distributed analytics with provable guarantees. Publications & Trends His publications since 2016 emphasize secure multi-party computation (SMCQL, 2016), differentially private indexing (Longshot, 2023), and privacy-utility trade-offs in federated databases. Key subfields include private data sharing , access pattern security , and incremental computation for outsourced systems. Awards & Grants 2022: Cisco Systems grant for "A Usable and Shareable Tool for Software Threat Modeling" Teaching & Service Bater teaches courses like Database Systems , Dissertation Research , and Special Topics in Data Infrastructure . He served on Tufts' CS PhD Admissions Committee (2022) and as a reviewer for the ACM Conference on Computer and Communications Security (2022).
Azza Abouzied is Associate Professor of Computer Science at New York University Abu Dhabi and Global Network Associate Professor at the Tandon School of Engineering. She serves as Vice Provost for Faculty Advancement and Engagement at NYUAD starting September 2024. Her research bridges database systems and human-computer interaction, focusing on intuitive tools for data querying and decision-making in uncertain, collaborative environments. PhD, Yale University (2013) MPhil, Yale University MSc, Dalhousie University BSc, Dalhousie University Her research centers on human-data interaction, designing systems that make data accessible to non-experts. She combines techniques from UI design, machine learning, and databases to build tools that simplify complex data tasks. Her earlier work focused on example-driven querying and synthetic data generation, while her recent work explores in-database prescriptive analytics and decision support in domains like disinformation mitigation and epidemic planning. Her publications span database and HCI venues, with a recurring theme of enhancing usability without sacrificing scalability. She co-founded Hadapt, a Big Data analytics platform, and has led interdisciplinary research through the Human-Data Interaction Lab and the Center for Interacting Urban Networks. Her teaching includes foundational courses such as Database Systems, Operating Systems, and Data, as well as the critical thinking course Techruption. VLDB Test of Time Award (2019) Best Paper Award in Database Systems Honorable Mention in HCI Publications Azza mentors undergraduate capstone students and advises prospective PhDs, research assistants, and postdocs. She is actively involved in academic leadership, having chaired NYUAD’s faculty council in 2024 and co-chaired the SIGMOD 2025 program. Her work emphasizes empowering users to critically engage with data and AI, both in research and education.
Catia Pesquita is an Associate Professor in Computer Science at the Faculty of Sciences of the University of Lisbon , where she is also a Senior Researcher at LASIGE and leads the Health and Biomedical Informatics Research Line . With a multidisciplinary background in Biology and Computer Science, she focuses on Artificial Intelligence and Data Science applications in life and health sciences . Her research spans Semantic Web , Biomedical Ontologies , Knowledge Graphs , and Explainable AI , with significant contributions to ontology matching and semantic similarity . Education: PhD in Computer Science - Bioinformatics (2012) MSc in Bioinformatics (2008) Degree in Cell Biology and Biotechnology (2005) Current Projects: KATY (2021-2024): AI-Empowered Personalized Medicine for cancer treatments. BRAINTEASER (2021-2024): AI for ALS and MS disease progression models. Research Outputs: Developed tools like AgreementMakerLight (AML) , KGsim-benchmark , and the Epidemiology Ontology . Over 133 publications with significant citations (32,909 reads, 3,889 citations). Teaching: Lectures advanced topics in Databases , Data Integration , Bioinformatics , and Big Data . Advocacy: Vice-president of Biodata.pt , promoting biological data valorization in Portugal. Actively involved in initiatives to promote computer science careers to young women .
Jorge Camba is an Associate Professor at the School of Engineering Technology and holds a courtesy appointment in the Department of Computer Graphics Technology at Purdue University . He also serves as a Senior Research Scientist (by courtesy) in the Department of Industrial Engineering at the University of Naples Federico II , Italy. PhD in Systems and Engineering Management (Universidad Politécnica de Valencia, Spain) MSc in Digital Media (East Tennessee State University) MSc in Computer Science (Universidad de Vigo, Spain) His research explores intelligent CAD systems , digital manufacturing , and mixed reality environments , focusing on model quality assurance , design intent communication , and collaborative design tools . Recent work investigates spatial cognition in CAD education , geometric variability analysis , and annotation-driven knowledge management . Key trends in his publications include parametric modeling strategies , 3D annotation systems , and XR applications in design evaluation. Awards include the Purdue Faculty Scholar (2021) and I3B Fellow (2021). He has presented at conferences on topics like Industry 4.0 , space habitat design , and digital product quality .
Catherine Arnott Smith is a Professor at the University of Wisconsin–Madison’s School of Computer, Data & Information Sciences (Information School). Her expertise spans consumer health informatics, medical informatics, and health information management. She holds advanced degrees from the University of Michigan (AMLS, M.A. 1992) and University of Pittsburgh (MS 2000; PhD 2002). Research focuses on patient-facing health technologies, including clinical note accessibility, health literacy, and public library roles in health information provision. Key areas include pediatric open notes, wearable device terminology, and disability-related health management. She teaches courses such as Digital Health (LIS 517) and Information Organization (LIS 602). Publications emphasize healthcare transparency, misinformation mitigation, and stakeholder engagement. Recent work explores cognitive/cultural factors influencing vaccination attitudes and parent perspectives on pediatric clinical notes. She advocates for inclusive design of health IT systems, leveraging qualitative methods like interviews and content analysis. No scientific awards are explicitly listed, but her extensive grant-funded research addresses topics like health information equity and consumer engagement. She collaborates with interdisciplinary teams in medical, library, and educational sectors. Her work bridges clinical documentation practices with public health needs, particularly for vulnerable populations such as disabled youth and low-literacy caregivers.
Professor Zhifeng Bao is a faculty member at RMIT University's School of Computing Technologies. His research focuses on enhancing data usability across heterogeneous domains, including structured, unstructured, and spatial-temporal data. His work spans database management, keyword search optimization, social network analysis, and spatio-textual data processing. He coordinates the course COSC1169: Intranet and Internet Data Engineering and supervises PhD/Masters students in projects such as trajectory data processing, data asset valuation, and edge computing optimization. Research interests emphasize improving data accessibility and efficiency through methodologies like query relaxation, visual analytics, and provenance tracking. His recent projects include cost-effective edge node placement, traffic accident risk prediction, and differentially private federated learning. Teaching and supervision activities highlight a commitment to bridging theory and practical data engineering challenges.
