Hadi Esmaeilzadeh is an Associate Professor at the University of California, San Diego in the Department of Computer Science and Engineering . He previously served as an Assistant Professor at Georgia Tech (2013-2017) and holds the Halicioğlu Chair in Computer Architecture . As founder/director of the Alternative Computing Technologies (ACT) Laboratory and associate director of UCSD's Center for Machine Integrated Computing and Security (MICS) , his research drives cross-stack solutions for next-generation computer systems. Early tenure recipient at UCSD Coined the term "dark silicon" in computer architecture Developed Tabla/DnnWeaver open-source frameworks Research Interests span: Approximate Computing Neural Acceleration FPGA/ASIC Hardware Design Machine Learning Systems Dark Silicon Challenges Security/Privacy in Accelerated Systems Scientific Recognition : 4 CACM Research Highlights 4 IEEE Micro Top Picks Distinguished Paper Award (HPCA 2016) Inducted to ISCA Hall of Fame (2018) Teaching : Developed courses on accelerator design (CSE 240D) and alternative computing (CS 8803 ACT) Advocates for hands-on FPGA-based learning in Processor Design with FPGAs courses
Tianyi Zhang is a Tenure-Track Assistant Professor in the Department of Computer Science at Purdue University, part of the College of Science. He leads the Human-Centered Software Systems Lab, focusing on AI-driven systems that synergize human expertise with machine intelligence to enhance programming productivity and software reliability. Prior to Purdue, he was a Postdoctoral Fellow at Harvard University under Dr. Elena Glassman and earned his Ph.D. from UCLA (2019) and B.Sc. from Huazhong University of Science and Technology (2013). Education: Ph.D. in Computer Science, University of California, Los Angeles (2019) Bachelor's in Computer Science, Huazhong University of Science and Technology (2013) Research Interests: His work spans Software Engineering, Human-Computer Interaction, and AI. Key areas include program synthesis, interactive debugging tools, autonomous driving system testing, and mitigating biases in AI models. He develops systems like Interpretable Program Synthesis and SQLucid to bridge human and machine intelligence. Recent Trends in Publications: Recent work emphasizes human-in-the-loop AI, including mixed-initiative systems for data wrangling (Dango), interactive program repair, and bias analysis in text representations (STILE). He also explores challenges in autonomous driving testing and LLM-based code generation errors. Awards & Grants: NSF Career Award (2024) Amazon Research Award Showalter Trust Research Award for pre-diabetes research $1.5M NSF grant for software supply chain security Best Paper Honorable Mentions at CHI and VAHC Advising & Teams: Supervises 12+ PhD/Master's students and 30+ research interns. Notable advisees include Bonan Kou (API misuse studies) and Yuan Tian (text-to-SQL systems). Collaborates with Harvard Medical School on healthcare data analysis. Labs & Initiatives: Directs Purdue's Human-Centered Software Systems Lab. Co-founded the Societal Impact Fellows program. Active in open-source projects like Examplore for API usage visualization and JShrink for Java debloating.
Christopher Kruegel is a Professor in the Department of Computer Science at the University of California, Santa Barbara (UCSB), where he conducts research in systems security. He is affiliated with the International Secure Systems Lab (iSecLab) and was a co-founder of Lastline, Inc., which was acquired by VMware in 2020. His work focuses on creating practical security solutions that address real-world problems through system building and experimental validation. Professor Kruegel's research spans multiple areas of computer security including malware analysis, web security, network security, and vulnerability analysis. His work often involves developing systems that analyze programs for malicious behavior, scanning web applications for vulnerabilities, and improving privacy on social networks. He has made significant contributions to firmware security, smart contract analysis, and mobile security through his extensive publication record. His 15 most recent publications (2022-2023) demonstrate a strong focus on practical security solutions across diverse domains including firmware security (Shimware, Fuzzware), blockchain and smart contract security (Confusum Contractum, NFT security), mobile security (TEEzz, Columbus), and vulnerability detection techniques (Actor, Toss a Fault to Your Witcher). His research shows consistent innovation in security tools and methodologies with several papers receiving distinguished awards. Fellow of the Institute of Electrical and Electronics Engineers Outstanding Graduate Mentor Award, UCSB Academic Senate Distinguished Artifact Award for Fuzzware paper Distinguished Paper Award for Ramblr paper Best Student Paper Award for Detecting Spammers On Social Networks Professor Kruegel has advised numerous graduate students and collaborated extensively with researchers at UCSB and beyond. His work has been funded by various research grants supporting his security research initiatives. He is actively involved in the International Secure Systems Lab, which focuses on practical security solutions for real-world problems.
Jennifer Widom is the Frederick Emmons Terman Dean of Stanford University's School of Engineering and holds the Fletcher Jones Professorship in Computer Science and Electrical Engineering. She previously served as Chair of the Computer Science Department (2009–2014) and Senior Associate Dean (2014–2016). Widom earned her Ph.D. in Computer Science from Cornell University (1987) and completed her undergraduate degree in Music at Indiana University (1982). She joined Stanford in 1993 after research at IBM Almaden. Education: Ph.D., Computer Science, Cornell University, 1987 MS, Computer Science, Cornell University, 1985 MS, Computer Science, Indiana University, 1983 BS, Music, Indiana University Jacobs School of Music, 1982 Research Interests: Widom's work focuses on nontraditional data management, including data streams, uncertain databases, crowdsourcing, and query processing systems like STREAM and Deco . She has pioneered methods for managing and querying uncertain data, optimizing graph algorithms, and integrating human computation into data systems. Key Contributions: Developed the STREAM system for real-time data stream management Advanced techniques for crowdsourcing quality management Contributed to foundational work in uncertain databases and provenance tracking Awards & Recognition: ACM Fellow (2005) Member, National Academy of Engineering (2005) Edgar F. Codd Innovations Award (2007) ACM-W Athena Lecturer (2015) EPFL-WISH Erna Hamburger Prize (2018) Teaching & Leadership: Widom teaches courses on data analytics and database systems, advising students like Arnav Joshi. She has led major initiatives in computational education and institutional leadership at Stanford.
Lorin M. Hitt serves as the Zhang Jindong Professor of Operations, Information and Decisions at the University of Pennsylvania's Wharton School. His distinguished career spans multiple domains at the intersection of information technology, economics, and business strategy. As a leading scholar in IT productivity and innovation, he maintains an active research agenda while teaching undergraduate and graduate courses in information systems and data analysis. Professor Hitt's research interests focus on the relationship between information technology and productivity, with particular emphasis on complementary factors such as organizational design and human capital that affect the value of IT investments. His current work explores the economics of IT labor mobility, enterprise software contracting, recommender systems' influence on consumer behavior, measurement of intangible assets, and pricing information goods. His research increasingly examines IT deployment in healthcare settings and the role of the IT workforce in relation to issues like offshoring and the H1-B visa program. His research publications demonstrate consistent contributions to top-tier journals, with recent work analyzing how data analytics mitigates post-IPO innovation decline, digital capital accumulation in superstar firms, and the relationship between analytics skills and firm productivity. His publication record shows sustained scholarly output across multiple domains of information systems research. Best Paper Runner-up – Management Science, 2014 Best Paper – Information Systems Research, 2013 Multiple Wharton Excellence in Teaching Awards (2003, 2007-2008, 2011) David Hauck Award for Distinguished Teaching, 1999 National Science Foundation Career Grant Recipient, 1998 Lindback Award for Distinguished Teaching, 1998 Professor Hitt teaches multiple courses including OPIM101 (Introduction to OPIM), OPIM105 (Data Analysis in VBA and SQL), OPIM469 (Information Strategy and Economics), and OPIM955 (Doctoral Seminar in IS Economics). His teaching focuses on information systems management, economics, data analysis, and advanced analytical methods. Beyond academia, he consults on IT outsourcing agreements and IT investment evaluation, and occasionally serves as an expert witness in technology-related litigation.
Jeff Offutt is a Professor and Chair of the Department of Computer Science at the University at Albany, College of Nanotechnology, Software, & Engineering. Previously, he was a Full Professor with Tenure in Software Engineering at George Mason University since 2005. He received his PhD in Information & Computer Science from the Georgia Institute of Technology in 1988. His research spans software testing, mutation testing, model-based testing, automatic test data generation, web application testing, and software engineering education. He has led significant projects such as the NSF-funded integration of CS into K-5 classrooms and the Google-funded SPARC project for scalable CS1/CS2 instruction. The 15 most recent articles reflect a continued focus on mutation testing cost reduction, model-based testing oracles, educational innovations, and security aspects of web applications. Trends include empirical validation, industrial applicability, and bridging theory with practice in software testing and engineering education. John Toups Presidential Medal for Excellence in Teaching (2020) George Mason University’s Alumni Association Faculty Member of the Year (2020) Outstanding Faculty Award from the State Council of Higher Education for Virginia (2019) Best Paper Award at ICST 2021 10-Year Most Influential Paper Award at MODELS 2020 George Mason University Teaching Excellence Award (2013) ACM Notable Article Award (2013) Jeff Offutt has mentored numerous graduate students including Upsorn Praphamontripong, Nan Li, and Yu-Seung Ma, and has led major grant-funded projects such as the SPARC educational model and NSF initiatives on K-5 CS integration. His textbook Introduction to Software Testing (with Paul Ammann) is widely adopted globally. He led the MS in Software Engineering program at GMU and developed several new courses in software testing, web engineering, and usability. He pioneered innovative teaching methods using web technologies and asynchronous learning models. He also co-founded the IEEE International Conference on Software Testing, Verification and Validation (ICST) and served as Editor-in-Chief of Software Testing, Verification and Reliability from 2007 to 2019.
Scott Fraundorf is an Associate Professor in the Department of Psychology at the University of Pittsburgh , where he leads the MAPLE (Memory And Psycholinguistics in Learning & Education) Lab . He combines cognitive science and data science to study human behavior prediction, educational program evaluation, and psycholinguistics . His research focuses on student learning and metacognition language processing and educational technology interventions statistical modeling using regression , machine learning , and mixed-effects models as well as open-source tool development for cognitive science. Key technical skills include Python , R , and SQL programming, with 3 patents for intelligent tutoring systems in English grammar. He has mentored over 70 graduate students and faculty in quantitative methods.
Dr. Chenhao Ma is an Assistant Professor at the School of Data Science , The Chinese University of Hong Kong, Shenzhen , where he works on large-scale data management and data mining. Previously, he was a Postdoctoral Fellow at the University of Hong Kong (2021–2022) and earned his PhD in Computer Science from the University of Hong Kong (2021) and B.Eng. from Shandong University (2017). Current research focuses on graph computing (dense subgraph discovery, motif analysis, graph learning), AI+DB (Text-to-SQL, vector search), and traffic data mining (trajectory analysis, outlier detection). He has published over 40 papers in top venues including SIGMOD, PVLDB, KDD and received the ACM SIGMOD Research Highlight Award 2021 and Best of SIGMOD 2020 (4/458). Scientific Awards : ACM SIGMOD Research Highlight Award 2021 Best of SIGMOD 2020 (4/458) Presidential Young Fellow at CUHK-Shenzhen (2023) Hong Kong and China Gas Scholarship (2019-2020) Reaching Out Award (2019) HKU Postgraduate Scholarship (2017-2021) ACM-ICPC Gold Medal (2015) National Scholarship (2014, 2015) Advising and Research Team : He leads a team including Postdoc Dr. Yuanyuan Zeng, PhD students Lujie Ban, Yuwei Xu, and MPhil students Yi Yang, Yuyang Liang. Former mentees like Yichen Xu (PhD at Berkeley) and Jiayang Pang (Master at UC San Diego) have achieved academic placements. Professional Service : He has served as PC member/reviewer for VLDB, KDD, ICDE, WWW, NeurIPS, TKDE , and guest editor for Applied Sciences and Frontiers in Big Data . He chairs sessions at ICDE and VLDB.
Amir Gilad is a Scharf-Ullman endowed Assistant Professor (Senior Lecturer) at the Hebrew University of Jerusalem’s School of Computer Science and Engineering. His research focuses on responsible data science, including causal inference, differential privacy, fairness in data, and tools for data analysis. He holds a Ph.D. in Computer Science from Tel Aviv University, where he was advised by Prof. Daniel Deutch. Prior to this, he was a postdoctoral researcher at Duke University, mentored by Prof. Sudeepa Roy, Prof. Ashwin Machanavajjhala, and Prof. Jun Yang. Education: Ph.D. in Computer Science, Tel Aviv University (Advisor: Daniel Deutch) MSc in Computer Science, Tel Aviv University BSc in Mathematics and Computer Science, Tel Aviv University His research interests span data quality assessment, private and fair data generation, and causal inference applications . He has received notable awards, including the 2024 Alon Scholarship and the 2019 Google Ph.D. Fellowship. Recent Projects: Developing algorithms for data quality repair and assessing bias in datasets Generating differentially private data that satisfies fairness constraints Applying causal inference to enhance data analysis tools Awards and Honors: 2024 Alon Scholarship for Outstanding Faculty Integration 2019 Google Ph.D. Fellowship in Structured Data 2018 SIGMOD Research Highlight Award 2017 VLDB Best Paper Award Teaching: Courses include “Topics in Responsible Data Science” and “Seminar on Causal Inference in Data Analysis” at Hebrew University, and “Extended Introduction to Computer Science” at Tel Aviv University. He has also led workshops on Google Technologies. Labs & Teams: His work is centered around the School of Computer Science and Engineering’s database group, focusing on foundational and applied aspects of privacy-aware data systems.
Bill Howe is an Associate Professor at the University of Washington's Information School, with adjunct appointments in Computer Science & Engineering and Electrical Engineering. He serves as Founding Program Director and Faculty Chair of the UW Data Science Masters Degree, Founding Associate Director and Senior Data Science Fellow at the UW eScience Institute, Director of the Urbanalytics Lab, and Co-Founding Director of the Center for Responsible AI Systems and Experiences. He also co-founded Urban@UW and created the first Data Science MOOC through Coursera. His research focuses on making data science accessible in public sector applications with emphasis on equity, privacy, and compliance. Current interests include: Algorithmic fairness in urban and social contexts Privacy-preserving synthetic data generation Machine learning for heterogeneous data Database systems and high-performance computing Human-computer interaction for data systems Responsible AI development and deployment Publication analysis reveals strong focus on responsible data science, with recent work emphasizing differential privacy, COVID-19 data equity, urban mobility fairness, and relational data systems. Earlier foundational work established contributions to scientific workflow systems and data pricing models. Awards and Honors: Runner-up Best Paper Award (VLDB 2023) Best Paper Award (SIGMOD 2019) Best Paper Award (VIs 2019) Best Paper Award (InfoVis 2018) He leads the Urbanalytics Lab and advises multiple students including An Yan (fairness in urban mobility), Sean Yang (machine learning embeddings), and Dominik Moritz (visualization systems). His projects span EZLearn for automatic claim validation, privacy-preserving synthetic data, and Myria middleware for polystores.
Shiva Jahangiri is an Assistant Professor in the Department of Computer Science and Engineering at Santa Clara University's School of Engineering. His research focuses on Big Data Management Systems, Databases for AI/ML, and Query Optimization. He leads the DBIS Lab, which explores database internals, vectorized data processing, and open-source projects like Apache AsterixDB. Education: Ph.D. in Computer Science from the University of California, Irvine; M.S. in Computer Science (Data Science) from the University of Southern California. Current courses taught include Advanced Programming, Advanced Database Systems, and Introduction to Database Systems. He advises Ph.D. and Master’s students on topics like Vector Databases, Query Scheduling, and Resource Management. Recent research trends involve optimizing group-by/aggregation operators, schema inference for semi-structured data, and memory management in complex join queries. His work bridges theoretical advancements with practical implementations in open-source systems. DBIS Lab activities include student participation in senior design projects, directed research, and volunteer roles. The lab emphasizes industry collaboration for hands-on experience in database systems development.
Farah Kamw is an Assistant Professor in the Department of Computer Science at Wayne State University. With a PhD in Computer Science (2019) from Kent State University, her expertise spans 18 years of software development, academic teaching, and research in information visualization and database management. Education : PhD (Kent State), MSc (University of Zakho), BSc (University of Baghdad) Her research focuses on Information Visualization and Visual Analytics of spatial-temporal data, particularly through 8 publications (2013-2021) addressing urban mobility patterns, trajectory analysis, and geospatial data integration. She has developed several open-source visual analytics tools including TrajAnalytics and SparseTrajAnalytics, applying both document and graph database techniques. Farah teaches core Computer Science courses such as Algorithm Design , Programming Languages , and Database Systems . Her technical skills include Python, C++, Java, SQL, NoSQL databases, and GIS technologies.
Arnab Nandi is a Professor of Computer Science and Engineering at The Ohio State University, with a courtesy appointment in Biomedical Informatics. He holds leadership roles including Steering Committee Member for the Human-in-the-Loop Data Analytics (HILDA) Workshop and has served as Workshops co-chair for SIGMOD 2025-26 and Demonstrations co-chair for SIGMOD 2024. His research focuses on bridging human interaction and data infrastructure, spanning database systems, human-in-the-loop data analytics, and next-generation query interfaces. Nandi's work emphasizes interactive data exploration through projects like DICE (Distributed Interactive Cube Exploration), GestureDB (Querying Beyond Keyboards), and Omni (Multimodal Data Exploration). His recent research explores integrating LLMs into database education, augmented reality interfaces for data analytics, and multimodal approaches to video querying. Nandi has received numerous honors including the NSF CAREER Award, Google Faculty Research Award, IEEE TCDE Early Career Award, and the University's Alumni Award for Distinguished Teaching. He was also named to Columbus Business First's '40 under 40' and became an ACM Distinguished Member in 2024. As an educator, he teaches courses including CSE 3241 (Introduction to Database Systems), CSE 5242 (Advanced Database Systems), and CSE 5251 (Introduction to Software Startups). His educational innovations include DBTutor, which integrates LLMs into database systems education. At Ohio State, Nandi co-founded the OHI/O Program, which fosters tech culture through hackathons, and The STEAM Factory, an interdisciplinary research collaboration network. Prior to academia, he was founder and CEO of Mobikit, a connected vehicles data analytics startup acquired by Azuga Inc. (a Bridgestone company). His research has been supported by the NSF and industry partnerships, with applications spanning precision agriculture (CropFusion), clinical data pipelines (ICARUS), and interactive visualization systems (Perceptvis).
Gerome Miklau is a Professor of Computer Science at the University of Massachusetts Amherst, affiliated with the Manning College of Information and Computer Sciences (CICS). He leads the DREAM Lab and focuses on privacy, security, and equitable data management, particularly in differential privacy and fair data analysis. His work includes designing algorithms for private data synthesis, privacy-preserving SQL engines, and auditing systems like AuditGuard. He co-founded Tumult Labs to commercialize privacy technology and advised the U.S. Census Bureau on privacy for the 2020 decennial census. Education: Ph.D. in Computer Science from the University of Washington (2005), B.S. in Mathematics and Rhetoric from UC Berkeley (1995). Research Interests: Differential privacy, secure data management, fairness in algorithms, privacy-preserving data synthesis, and forensic database analysis. His lab develops tools like Ektelo and PrivateSQL, addressing challenges in privacy-accurate tradeoffs and scalable private data processing. Awards: 2006 ACM SIGMOD Dissertation Award, 2007 NSF CAREER Award, 2013 ICDT Best Paper Award, and two ACM PODS Test-of-Time Awards (2020 and 2012). Grants & Service: Co-chair of OpenDP Advisory Board, steering committee member for TPDP workshops, and organizer of the 'Data, Responsibly' Dagstuhl workshop. His service includes program committees for SIGMOD, ICML, and FAT*. He teaches courses on databases, privacy, and programming. Labs/Teams: DREAM Lab (Data systems Research for Exploration, Analytics, and Modeling) and collaborations with the Center for Data Science and Cybersecurity Institute at UMass Amherst.
Zaman Noor is a Senior Lecturer in the Department of Computer Science and Engineering at The University of Texas at Arlington (UTA), where he has been serving since September 2022. He previously held an Adjunct Professor role at the same institution from January to August 2022 and was a Lecturer at Port City International University from 2015 to 2016. PhD in Computer Science, The University of Texas at Arlington, 2021 BS in Computer Science, Chittagong University of Engineering, 2019 His research focuses on Distributed Systems , Large Scale Computation , and Big Data Analytics , with an emphasis on optimizing array-based programming models for distributed environments such as Spark SQL. He explores compiler techniques for translating high-level array operations into efficient distributed queries, enabling scalable data analytics. Zaman Noor's publications reveal a strong trend in bridging programming languages with database systems, particularly through the translation of array-based loops and graph programs into optimized SQL-based distributed execution plans. His work intersects computer science, database systems, and high-performance computing, targeting applications in cloud computing, big data management, and machine learning infrastructure. He has received recognition for his research, including the Best Paper Award at IEEE BigData Congress in 2018 . Best Paper Award, IEEE BigData Congress, July 6, 2018 Zaman Noor advises master's students, including Priyank Gupta, and serves on thesis committees. He is actively involved in teaching and curriculum development, offering courses in distributed systems, cloud computing, and algorithms. He also contributes to academic service as a faculty advisor for the Google Developer Student Club and The Cornerstone, and as a member of the Publicity Committee. He leads and participates in educational initiatives and student mentorship, particularly in cloud computing and big data technologies, and supports student research through thesis supervision and committee roles. Zaman Noor is affiliated with research and teaching teams focused on data-intensive systems and distributed computing. His GitHub profile indicates engagement with open-source machine learning frameworks, including contributions to TensorFlow-related repositories, reflecting his interest in practical implementations of large-scale computation.
