Programmatic Data Labeling Professors and Supervisors

Steve MussmannView profile

Assistant Professor

Georgia Institute of TechnologyUnited States

Steve Mussmann serves as an Assistant Professor in the School of Computer Science at the Georgia Institute of Technology, where he joined in Fall 2024. His research centers on data-centric machine learning, with emphasis on active labeling, data selection, and adaptive experimental design methodologies. He maintains active collaborations through Georgia Tech's Foundations of AI (FoAI) and ML@GT research groups. Mussmann earned his PhD in Computer Science from Stanford University in 2021 under Percy Liang's supervision, following a BS in Math, Statistics, and Computer Science from Purdue University in 2015. His professional trajectory includes a machine learning researcher role at Coactive AI and an IFDS postdoctoral fellowship at the University of Washington's Paul Allen School of Computer Science and Engineering. His research program investigates theoretical and practical aspects of data efficiency in machine learning systems, particularly focusing on active learning frameworks, statistical properties of data algorithms under concept drift, and task specification via prompts or demonstrations. Current projects address challenges in label-efficient training of large language models and multimodal dataset development. Analysis of his 15 most recent publications reveals a consistent focus on advancing data-centric methodologies, with increasing emphasis on large-scale applications like multimodal datasets and language model fine-tuning. His work bridges theoretical guarantees in experimental design with practical frameworks like LabelBench for benchmarking label efficiency. Mussmann has received recognition through the IFDS postdoctoral fellowship. His contributions to the field include foundational work on active learning theory and data selection algorithms. IFDS postdoctoral fellow He currently advises five graduate students including PhD candidates Kangping Hu (CS) and Hangyu Zhou (ML), alongside MS students Kabir Kang and Kalp Vyas, and undergraduate Saloni Bedi. Former advisee Wei-Liang (Edison) Liao completed BS research under his supervision. His teaching portfolio includes graduate courses CS 7545 (Machine Learning Theory) and CS 8803-DML (Data-centric Machine Learning). Mussmann operates within Georgia Tech's Foundations of AI initiative and ML@GT collective, which provide infrastructure for large-scale data-centric research. His lab develops open-source tools like LabelBench for reproducible evaluation of data selection techniques, with ongoing projects exploring video data exploration systems and adaptive finetuning frameworks for foundation models.

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Stephen BachView profile

Assistant Professor

Brown UniversityUnited States

Stephen Bach is an Assistant Professor in the Computer Science Department at Brown University, where he leads the BATS (Bach's Awesome Team of Students) research group. His research focuses on improving how humans teach computers through programmatic weak supervision and methods for learning from fewer examples like zero-shot and few-shot learning. His primary research interests include weak supervision, data programming, probabilistic soft logic (PSL), statistical relational learning (SRL), information extraction, zero-shot learning, and few-shot learning. Bach's work often focuses on exploiting high-level, symbolic or semantically meaningful domain knowledge, with applications in information extraction, image understanding, scientific discovery, and data science. Bach's recent publications show a strong focus on language models, weak supervision techniques, and multimodal learning, particularly examining the capabilities and limitations of models like CLIP. His research has increasingly emphasized practical applications in low-resource settings and cross-lingual scenarios. Best Paper Award at NeurIPS Workshop on Socially Responsible Language Modelling Research (SoLaR) 2023 Larry S. Davis Doctoral Dissertation Award Selected for oral presentation at ICLR 2024 Best of VLDB 2018 paper selection Bach advises numerous Ph.D., Master's, and undergraduate students, many of whom have gone on to positions at leading tech companies, research institutions, and graduate programs. His research group has developed several influential frameworks including Snorkel (for weak supervision), PSL (Probabilistic Soft Logic), T0 (for zero-shot task generalization), ZSL-KG (for zero-shot learning with knowledge graphs), TAGLETS (for semi-supervised learning with auxiliary data), and WISER (for programmatic weak supervision in sequence tagging).

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Eric WideraView profile

Professor

University of California, San FranciscoUnited States

Eric Widera is a Professor of Clinical Medicine in the Division of Geriatrics at the University of California San Francisco (UCSF) School of Medicine. He serves as Director of the Hospice & Palliative Care Service at the San Francisco VA Medical Center, where he leads clinical, educational, and programmatic initiatives. He is a nationally recognized clinician-educator with leadership roles in the American Academy of Hospice and Palliative Medicine (AAHPM) and the Association of Directors of Geriatrics Academic Programs (ADGAP), where he served as past president. Dr. Widera completed his education with a B.S. in Biology from the University of California, Irvine, an M.D. from UCSF, followed by residency in Internal Medicine at Mount Sinai Hospital and fellowship in Geriatric Medicine at UCSF. He further enhanced his academic skills through the Teaching Scholars program and Diversity, Equity, and Inclusion Champion Training at UCSF. His research and academic focus centers on improving care for older adults with serious illness through educational innovation, prognostication, communication, and policy. He is deeply engaged in medical education, having directed the Geriatrics Fellowship at UCSF for over a decade and currently mentoring residents, fellows, and pharmacy trainees. A key interest is the role of digital media in medical education, exemplified by his co-founding of GeriPal, a leading podcast and blog, and ePrognosis, an online prognostic calculator tool. His recent publications span palliative care, Alzheimer’s disease, medical ethics, and health policy, often addressing critical issues in aging and end-of-life care. His scholarly output is extensive and impactful, with recent articles in JAMA , JAMA Internal Medicine , and The New England Journal of Medicine . The body of his work demonstrates a consistent focus on practical clinical challenges, ethical dilemmas, and system-level improvements in care for vulnerable older populations. Themes include prognostic communication, advance care planning, dementia care, and the integration of palliative services across specialties. Dr. Widera has received numerous scientific awards recognizing his excellence, including: Hastings Center Cunniff-Dixon Physician Award (2011) AAHPM Hospice and Palliative Medicine Leaders Under 40 (2015) PDIA Palliative Medicine National Leadership Award (2014) "Visionary in Hospice and Palliative Medicine" Award (2018) Excellence in Teaching Award, Academy of Medical Educators, UCSF (2022) Master Clinician, Council of Master Clinicians, UCSF (2024) He has been the recipient of multiple grants, including the Geriatric Academic Career Award (GACA), and his work has influenced national policy and clinical guidelines. He is an active advisor and educator, shaping the next generation of geriatrics and palliative care leaders. Through his clinical leadership, educational programs, digital platforms like GeriPal, and national advocacy, Dr. Widera plays a pivotal role in advancing the fields of geriatrics and palliative medicine. His work is supported by a robust interdisciplinary team at the San Francisco VA, and he collaborates widely with researchers across UCSF and nationally. His leadership in developing educational resources and digital tools has significantly expanded the reach and impact of geriatrics and palliative care knowledge.

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Gareth JamesView profile

Professor

Emory UniversityUnited States

Gareth James is the John H. Harland Dean and Professor of Information Systems & Operations Management at Emory University's Goizueta Business School, a position he assumed in July 2022. Prior to this, he held significant leadership roles at USC Marshall School of Business, including interim dean (2019-2020), vice dean of faculty, and deputy dean (2020-2022). James received his PhD in Statistics from Stanford University, complemented by a BComm in Finance and BS in Statistics from The University of Auckland. His academic journey reflects a strong foundation in both statistical methodology and business applications. James specializes in statistical problems in marketing, functional data analysis, statistical methodology, and high-dimensional regression. His research bridges theoretical statistics with practical business applications, particularly in the areas of machine learning, advertising optimization, and technology adoption. His work demonstrates a consistent focus on developing methodological innovations that address real-world business challenges. His research contributions have been widely recognized, with over 30,000 citations. James has led multiple National Science Foundation research grants and served as associate editor for five top research journals. His most notable scholarly contribution is co-authoring the highly successful textbook "An Introduction to Statistical Learning," which has become a standard reference in the field. Fellow of the American Statistical Association Fellow of the Institute of Mathematical Statistics Dean's Research Awards (two times) Evan C. Thompson Faculty Teaching and Learning Innovation Award Three-time winner of the Marshall School of Business' Golden Apple Award Dean's Ph.D. Advising Award USC Mellon Award Provost's Mentoring Award James is equally distinguished as a mentor and educator. His teaching excellence is evidenced by multiple Golden Apple Awards, while his mentoring impact is reflected in the Dean's Ph.D. Advising, USC Mellon, Evan C. Thompson, and Provost's Mentoring awards. As Dean, he leverages his expertise in data-informed decision making to strengthen Goizueta's mission of preparing principled leaders to have a positive influence on business and society.

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Morana FudurićView profile

Associate Professor

University of ZagrebCroatia

Morana Fudurić, PhD is an Associate Professor at the Department of Marketing , Faculty of Economics & Business, University of Zagreb. She received her PhD in Communication Sciences from Universita della Svizzera italiana, focusing on social media relationship orientation and business performance. Education: Master's in Business Economics & Marketing Communication (University of Zagreb), PhD in Communication Sciences (Universita della Svizzera italiana) Research Interests : Computational advertising Data-driven marketing Integrated marketing communications Digital media consumption Technological disruption in media Consumer behavior in digital ecosystems Recent Article Trends explore generative AI ethics in academia, psychographic segmentation in private label consumption, algorithmic allocation in digital platforms, and political advertising personalization. Her work bridges behavioral experiments with big data analysis. Scientific Awards : EMAC Junior Faculty Visiting Program Award (2018) Professional Experience includes industry work at IMAGO advertising agency and Core media TV production, which she integrates into teaching. She serves as Vice President for Membership & Communication at the European Marketing Academy (EMAC).

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Bart BaesensView profile

Professor

KU LeuvenBelgium

Bart Baesens is a full professor at the Faculty of Economics and Business (FEB) at KU Leuven, affiliated with the Information Systems Engineering Research Group (LIRIS). He is a member of Leuven.AI, the university's artificial intelligence institute, and leads the education commission for the Master in Information Management program at FEB. His research focuses on machine learning, fraud detection, credit risk, network analytics, and explainable AI, with applications in finance, insurance, agriculture, and smart systems. Baesens is known for developing frameworks like IML4DQ and GARG-AML, and has contributed to foundational work in sensor data analysis and graph-based learning for AML systems. His recent research emphasizes practical applications of AI, such as improving museum archive accessibility via large language models and enhancing agricultural sustainability through precision irrigation systems. He collaborates with industry partners on projects like predictive maintenance optimization and customer lifetime value modeling, leveraging techniques like temporal fusion transformers and self-supervised anomaly detection. Baesens' work bridges academia and industry, with publications addressing ethical AI, model interpretability, and real-world fraud detection challenges. His contributions span over 150 peer-reviewed articles, including influential studies on credit scoring, social network analytics for insurance fraud, and multi-task learning for environmental monitoring.

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Rahul TelangView profile

Professor

Martin Luther University Halle-WittenbergGermany

Rahul Telang is a Professor of Information Systems and Management at the H. John Heinz III School of Public Policy and Management at Carnegie Mellon University. His work spans digital piracy , computer security , and the economics of information systems , with a focus on policy implications of technology. His research examines the interplay between online piracy and cybersecurity , including how malware propagates through infringing sites. He also investigates privacy regulation (e.g., app privacy labels ), search engine advertising , and the economics of software vulnerabilities . Recent papers analyze artificial intelligence in copyright policy and workforce analytics using occupational skill embeddings . While Telang’s articles frequently use empirical methods and natural experiments , no formal scientific awards are listed in the available data. His collaboration network includes scholars from Carnegie Mellon, Emory, Chapman University, and Indian School of Business, covering topics like ride-hailing congestion and data breach impacts . No student advising or lab affiliation details are explicitly mentioned.

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Apurva GandhiView profile

Researcher

Carnegie Mellon UniversityQatar

Apurva Gandhi is a Researcher at Carnegie Mellon University , affiliated with the School of Computer Science and the Computer Science Department. Their research focuses on Artificial Intelligence with applications in program synthesis , agentic tasks , and handwritten content analysis . Research Interests : Artificial Intelligence Machine Learning Natural Language Processing Program Synthesis Publications highlight expertise in web agents , AI-driven database systems , deepfake detection , and sequence modeling for cybersecurity . Contact : apurvag@andrew.cmu.edu

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Rahul TelangView profile

Professor

Carnegie Mellon UniversityQatar

Rahul Telang is a Professor of Information Systems at Heinz College, Carnegie Mellon University, with a courtesy appointment at the Tepper School of Business. He has been at Heinz College since 2002 and is currently the director of the PhD program at the Heinz College. Professor Telang is also co-director of the Initiative for Digital Entertainment Analytics (IDEA) and part of Cylab and the Institute for Infrastructure Protection (I3P). Professor Telang's research broadly focuses on how Information and Communication Technologies (ICTs) and digitization impact consumers, businesses, and policies. His work spans two major domains: Digital Media Industry and Economics of Information Security and Privacy. In the Digital Media domain, he examines how digitization and piracy affect content providers, distributors, and users, with the aim of shaping optimal copyright and intellectual property policy. In the security domain, he investigates the incentives of various parties (users, firms, and hackers), market failures, and policy frameworks for information security. His recent research shows a strong focus on bug bounty programs, vulnerability disclosure economics, data breach impacts on consumer behavior, digital media consumption patterns, and the economic implications of AI on copyright policy. His work combines empirical analysis with economic modeling to provide actionable insights for both industry and policymakers, often using randomized field experiments and large-scale data analysis. Sloan Foundation Industry Study fellowship Multiple Google Faculty awards NSF CAREER award for work on economics of information security Senior editor positions at Management Science, Information Systems Research, and MIS Quarterly Professor Telang has secured extensive external funding from the National Science Foundation, National Security Agency, and industry partners for his research. He has worked extensively with industry and policymakers on media digitization issues, consulting with major entertainment companies like Disney and Warner Brothers on streaming strategies. As deputy director of the "living analytics" (LARC) project, he directs research on digital and social media analytics in collaboration with Singapore Management University. Professor Telang co-directs the IDEA center for digital entertainment analytics and contributes to Cylab's research on cybersecurity economics. His work on the Security Behavior Observatory has created infrastructure for long-term monitoring of client machines to understand security behavior, providing valuable insights into user security practices and vulnerabilities.

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Moulinath BanerjeeView profile

Professor

University of Michigan-Ann ArborUnited States

Moulinath Banerjee is a Professor of Statistics at the University of Michigan, affiliated with the Department of Statistics within the College of Literature, Science, and the Arts (LSA). He holds a B.Stat and M.Stat from the Indian Statistical Institute and a Ph.D. from the University of Washington. His research focuses on non-standard statistical problems, empirical process theory, threshold estimation, graphical networks, and the intersection of statistics with machine learning, particularly in distributed computing and data integration. Education: B.Stat (Hons), Indian Statistical Institute, 1995 M.Stat (Mathematical Statistics and Probability), Indian Statistical Institute, 1997 Ph.D. in Statistics, University of Washington, 2000 Research Interests: Inference under shape-restrictions and non-differentiable models Statistics-ML interface, including transfer learning and weak supervision High-dimensional and low-dimensional statistical methods Applications in crime modeling, policy evaluation, and causal inference Recent work emphasizes statistical methodologies for modern challenges like distributed computing, data integration, and performativity in predictive systems. His articles span topics such as change-plane regression, Hawkes processes, and posterior drift in transfer learning, reflecting contributions to both theory and applied domains. Scientific Awards: 2011 IISA Young Investigators Award Fellow of the Institute of Mathematical Statistics (IMS) Fellow of the American Statistical Association (ASA) IMS Medallion Lecture Awardee (2024) Teaching includes advanced courses in statistical theory (e.g., Stats 610, 611, 612) and applied methods (e.g., Stats 412, 425). Collaborations span academia and industry, addressing interdisciplinary challenges in data science and statistical methodology.

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Ziyang LiView profile

Assistant Professor

Johns Hopkins UniversityUnited States

Ziyang Li is an Assistant Professor of Computer Science at Johns Hopkins University and a member of the Data Science and AI Institute. He holds a Ph.D. in Computer Science from the University of Pennsylvania (2025) and dual bachelor's degrees in Computer Science and Mathematics from UCSD (2019). Research Areas: Neurosymbolic Programming, AI4Code His research bridges programming languages and machine learning, focusing on neurosymbolic methods that combine symbolic reasoning with learning-based techniques. Applications span software security, computer vision, natural language processing, bioinformatics, and clinical decision-making. He developed Scallop , a neurosymbolic programming language, and Lobster , a GPU-accelerated framework for neurosymbolic applications, with impacts in cybersecurity and biomedical domains. Recent publications highlight neurosymbolic approaches for RNA structure prediction, Long COVID modeling, and safety-critical systems. His work emphasizes data-efficient learning, weak supervision, and hybrid AI for scalable reasoning. Scientific Awards : AWS Fellowship (2023) KPCB Fellows, Engineering (2018) NIH L3C Honorable Mention Award Li has mentored students including Jason Liu, Felix Zhu, and Eric Zhao, and served as Teaching Assistant for courses at UPenn and UCSD. He co-organized the TACPS Workshop and reviewed for NeurIPS, ICLR, and ICML.

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Xinlei ChenView profile

Professor

ESMT BerlinGermany

Xinlei Chen serves as Professor of Marketing at Cheung Kong Graduate School of Business (CKGSB), with prior faculty appointments at Shanghai Advanced Institute of Finance (SAIF) and University of British Columbia. His academic foundation includes a B.E. from Tsinghua University and Ph.D. in Marketing from University of Minnesota. His educational background features: B.E. in Engineering, Tsinghua University Ph.D. in Marketing, University of Minnesota (Carlson School) Chen's research centers on empirical industrial organization and consumer decision modeling, with specific expertise in pricing strategy, advertising effectiveness, channel management, and entertainment economics. His work bridges theoretical structural modeling with real-world applications in digital platforms, video gaming, and social networks, emphasizing how consumers process information and firms optimize competitive strategies in dynamic markets. Recent investigations examine gender dynamics in online competition and advertising spillovers in short-form video ecosystems. Analysis of his publication trajectory reveals consistent focus on marketing science fundamentals while evolving toward digital economy applications. Early work established foundations in movie economics and private label competition, transitioning to contemporary investigations of platform markets, mobile advertising fraud, and social network effects. His methodology combines rigorous econometric techniques with field experiments, particularly evident in 2025 ByteDance research and mobile ad fraud studies. His scientific recognition includes: MSI Young Scholar (2009) Two-time Long Term Impact Award Finalist (Society for Marketing Science, 2018 & 2020) Carlson School Dissertation Award (2003-2004) Multiple doctoral consortium fellowships (INFORMS, AMA-Sheth) Chen demonstrates extensive teaching impact across executive education, MBA, and doctoral programs at CKGSB, SAIF, and UBC, complemented by industry experience as project manager at ABB and China-Machine Building International. His business-oriented scholarship extends to authored books on Chinese internet markets and regular commentary in major business media. Current research activities include field experiments on cross-media advertising and pandemic exercise behavior through digital platforms.

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