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Sunita SarawagiView profile

Professor

• Machine Learning
• Data Analytics
• Databases
• +10 more

Sunita Sarawagi is a Professor at Computer Science and Engineering , IIT Bombay, and a member of AI Labs@CSE . She is also associated with the Center for Machine Intelligence and Data Science (CMInDS), which she founded in 2020. Education: PhD in Computer Science from UC Berkeley (Thesis: Query Processing in Tertiary Memory Databases), BTech in Computer Science from IIT Kharagpur Research Interests span machine learning , data analytics , graphical models , and structured learning , with applications in text segmentation, sequence modeling, domain adaptation, and human-in-the-loop systems. Her publications reveal a strong focus on integrating data mining with database systems , temporal data analysis , and information extraction using probabilistic methods. Professional Activities include serving on the IEEE John Von Neumann Medal committee (2017-), VLDB 2011 Research Track Co-chair , and multiple program committee roles at top conferences like ICML, KDD, and SIGMOD. Labs & Teams : Leads the SS Lab , a research group focused on probabilistic graphical models, sequence modeling, and data integration techniques.

Chua Tat SengView profile

Professor

• unstructured data analytics
• multimedia information retrieval
• recommendation systems
• +6 more

Chua Tat Seng is a Professor at the School of Computing, National University of Singapore (NUS), holding the KITHCT Chair Professorship since 2009. He serves as co-Director of the NExT++ Center, a joint research center between NUS and Tsinghua University focused on Extreme Search. His academic career spans over three decades at NUS, where he has held various leadership positions including Acting Dean of the School of Computing (1998-2000) and Acting Head of the Department of Information Systems & Computer Science (1996-1998). Professor Chua's research spans unstructured data analytics , multimedia information retrieval , recommendation and conversation systems , and emerging applications in e-commerce and fintech . He established the Lab for Media Search (LMS) at the School of Computing and has been instrumental in advancing multimodal learning and search technologies. His work bridges theoretical foundations with practical applications, particularly in developing trustable AI systems for real-world deployment. His recent publications demonstrate a strong focus on large language models for recommendation systems , multimodal learning , and generative AI applications . The research trends show increasing emphasis on LLM-based recommendation, multimodal understanding, and addressing fundamental challenges in AI reliability, fairness, and efficiency. His work spans theoretical advancements in representation learning to practical applications in e-commerce, finance, and healthcare domains. ACM SIGMM Technical Achievement Award 2015 Multiple Best Paper Awards across ACM Multimedia, IEEE Transactions, and MMM conferences (2007-2020) Professor Chua has supervised 37 PhD students since 2004, establishing himself as a dedicated mentor in the academic community. His research has been supported by substantial grants including NExT++ ($12 million), Base Metals Price Forecasting ($200,000), and Multilingual Multimodal Knowledge Graph ($500,000). He maintains active collaborations with Tsinghua University, University of Southampton, and industry partners like Four Elements Capital and Singapore Press Holdings. As co-Director of the NExT++ Center, he leads a major research initiative focused on Web Intelligence and User Empowerment. His visiting professorships at Tsinghua University (2017-present) and Zhejiang University (2021-present) reflect his international impact in the field of multimedia and AI research.

• Human-Computer Interaction
• Learning Sciences
• Educational Technology
• +2 more

Jennifer Olsen, PhD, is an Assistant Professor of Computer Science at the University of San Diego since 2020. She holds a PhD, MS, and BS in Human-Computer Interaction and Cognitive Science from Carnegie Mellon University, followed by postdoctoral research at the Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland. Her research focuses on the intersection of human-computer interaction, cognition, and education, emphasizing collaborative learning and educational technology design from both learner and instructor perspectives. Education: PhD in Human-Computer Interaction, Carnegie Mellon University MS in Human-Computer Interaction, Carnegie Mellon University BS in Cognitive Science, Carnegie Mellon University Research Interests: Dr. Olsen explores how collaboration supports learning, designs technologies to enhance educational practices, and investigates gaze-based metrics for understanding collaborative problem-solving. Her work spans gamified robotics, AI-driven orchestration systems, and virtual reality applications in vocational training. She emphasizes learner-centered design and the integration of social robots and virtual agents in pedagogical settings. Grants/Advising: While no specific grants or advisees are listed, her prolific publication record indicates active involvement in educational technology research and development. Her work addresses challenges in classroom orchestration, multimodal data analysis, and accessibility in educational robotics. Labs/Teams: Collaborates with interdisciplinary teams focused on educational technology, human-robot interaction, and adaptive learning systems. Her research leverages tools like FROG orchestration graphs and eye-tracking technologies to develop practical classroom solutions.

David BammanView profile

Associate Professor

• Natural Language Processing
• Cultural Analytics
• Computational Social Science
• +3 more

David Bamman is an Associate Professor in the School of Information at UC Berkeley, specializing in applying Natural Language Processing (NLP) and machine learning to cultural and social science questions. He leads research in born-literary NLP, computational humanities, and cultural analytics, with affiliated roles in EECS, Linguistics, and Computational Precision Health. Bamman holds degrees from Carnegie Mellon (Ph.D., 2015), Boston University (M.A., 2006), and University of Wisconsin-Madison (B.A., 1998). His work is supported by NEH, NSF, and industry grants. Educations: Ph.D. in Computer Science (2015), Carnegie Mellon University M.A. in Applied Linguistics (2006), Boston University B.A. in Classics (1998), University of Wisconsin-Madison Research Interests: NLP for underserved domains (e.g., literature, social media), coreference resolution, cultural analytics, and computational methods for studying literature and culture. Projects include LitBank and BookNLP datasets. Grants & Awards: Hellman Fellow (2019), Amazon Research Award (2017), NSF CAREER Award, and NEH funding. Teaching: Courses include Natural Language Processing (Info 159/259), Computational Humanities (INFO 190), and Applied NLP (INFO 256). His research group explores topics like racial representation in high school literature, Hollywood diversity metrics, and the sociocultural implications of LLMs. Bamman advises multiple PhD students and collaborates on datasets like CMU Book Summaries and 11K Latin Books.

Amitabha BagchiView profile

Professor

• Data Algorithmics
• Probability and Networks
• Theoretical Computer Science
• +7 more

Amitabha Bagchi is a Professor in the Department of Computer Science and Engineering at IIT Delhi. His research spans data algorithmics, probability, networks, and theoretical computer science, with applications in distributed systems, social networks, and AI-driven platforms. He has published extensively in leading venues such as SIGMOD, VLDB, ICDE, AAAI, and KDD, often collaborating with students and researchers on problems involving graph algorithms, fairness, and large-scale data analysis. Research Interests: His primary research interests include Data Algorithmics, Probability and Networks, Theoretical Computer Science, Distributed Algorithms, Graph Algorithms, and Machine Learning Theory. He investigates algorithmic foundations for real-world problems such as food delivery optimization, social network analysis, and efficient data structures for streaming and large graphs. Publication Trends: Recent publications focus on fairness in gig economy platforms, efficient solvers for graph Laplacians, generalization in neural networks, and temporal graph querying. His work combines theoretical rigor with practical impact, often involving GPU acceleration, distributed computing, and data-aware algorithm design. Scientific Service: Editor, Algorithms (2020–present) Editor, Journal of Discrete Algorithms , Elsevier (2006–2018) Guest Editor, special issue on Algorithms for Shortest Paths in Dynamic and Evolving Networks , Algorithms (2021) Volume Editor for proceedings of ESA, ATMOS, COCOON, and others Conference Leadership: He has served on numerous program committees and as chair for conferences including ESA (Engineering Track, 2016), ATMOS (2020), and ICALP (2019). His involvement spans algorithmic engineering, transportation optimization, and theoretical computer science forums. Teaching: He currently teaches COL863: Special Topics in Theoretical Computer Science on concentration inequalities and their applications. He has previously taught advanced courses in algorithms and data structures.

Alan RitterView profile

Associate Professor

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +4 more

Alan Ritter is an Associate Professor at the School of Interactive Computing , Georgia Institute of Technology, with additional affiliation to the Machine Learning Center . His research focuses on Natural Language Processing , particularly robust models across domains/languages with fewer labels and efficient resource use, plus data-driven dialogue agents for open-topic conversations. Research Interests : Robust NLP models, cross-lingual transfer, resource-efficient learning, dialogue systems, cultural bias measurement, and privacy-aware language models Students : Mentors Ph.D. students in Georgia Tech's ML and CS programs, including Junmo Kang, Yang Chen, and Duong Minh Le. Alumni include Fan Bai (Ph.D. 2023), Yang Chen (Ph.D. 2024), and Andrew Li (M.S. 2024). Awards : NSF CAREER Award, Amazon Research Award, ACL 2024 Best Social Impact Paper, IUI 2009 Best Student Paper. Recent Work : Studies training budget allocation between supervised and preference-based finetuning, cross-lingual information extraction, cultural bias in LLMs, and privacy risk mitigation in social media disclosures. Service : Served as Program Chair for NAACL 2025, Area Chair for multiple top-tier conferences (COLM, EMNLP, ACL, EACL, AAAI). Email : alan.ritter@cc.gatech.edu

Kai S. ExnerView profile

Professor

• Electrocatalysis
• Inorganic Chemistry
• Materials Science
• +7 more

Prof. Kai S. Exner is a Professor of Theoretical Inorganic Chemistry at the University of Duisburg-Essen's Faculty of Chemistry and a member of the Zentrum für Medizinische Biotechnologie (ZMB). His research focuses on electrocatalysis , particularly optimizing electrode materials for energy applications such as batteries, electrolyzers, and fuel cells. He employs computational methods like ab initio calculations and microkinetic modeling to bridge theoretical insights with experimental and industrial applications, emphasizing sustainable catalyst design without relying on noble metals. His work integrates basic research with applied medical biotechnology through ZMB collaborations, though his primary affiliation lies in the Faculty of Chemistry. Exner has pioneered concepts like the electrochemical-step asymmetry index and free-energy span model, which are critical for screening electrocatalysts. His research group, 'Theoretical Inorganic Chemistry,' actively explores mechanisms behind oxygen and chlorine evolution reactions, emphasizing selectivity control and reaction pathway complexity. Exner received the Gottschalk-Diederich-Baedeker-Preis (2024) for advancing the energy transition through foundational electrocatalysis research. He leads Aktuelles Projekt funded by third-party grants and collaborates with facilities like the Analytics Core Facility Essen (ACE) and Imaging Center Campus Essen (ICCE). His advisory role focuses on guiding theoretical and computational approaches in electrochemistry, and he has established a reputation for interdisciplinary research connecting computational chemistry with practical energy solutions.

Sean CaoView profile

Associate Professor

• Artificial Intelligence in Finance
• Machine Learning Applications
• Capital Market Research
• +5 more

Sean Cao serves as Associate Professor (with tenure) at the Robert H. Smith School of Business, University of Maryland, where he is Director and Co-founder of the AI Initiative for Capital Market Research. He also holds an affiliation as professor at Harvard Business School's D 3 Institute. His academic journey began with a Ph.D. from the University of Illinois at Urbana-Champaign. Dr. Cao's research focuses on the intersection of artificial intelligence and capital markets, with particular expertise in how machine learning transforms financial analysis, corporate disclosure practices, and investment decision-making. His work examines the evolving relationship between human analysts and AI systems, blockchain applications in financial reporting, and the strategic adaptation of corporate communications for machine readership. He has pioneered research on the "AI divide" among investor groups and developed frameworks for human-AI collaborative stock analysis. His publication portfolio spans top journals including Journal of Financial Economics, Review of Financial Studies, Journal of Accounting Research, and Management Science. The research demonstrates consistent thematic progression toward increasingly sophisticated AI applications in finance, with recent work exploring distributed ledger technologies for auditing, machine learning for extracting private information from disclosures, and the economics of greenwashing in ESG funds. His studies frequently combine textual analysis with traditional financial metrics to uncover novel market insights. Fama-DFA Prize from Journal of Financial Economics for best paper in capital markets and asset pricing Michael J. Brennan Award from Review of Financial Studies Deloitte Initiative for AI and Learning award for developing trustworthy AI for social equity PanAgora Asset Management's Dr. Richard A. Crowell Memorial Prize Multiple best paper awards from Midwest Finance Association, Global AI Finance Conference, and Asian Finance Association Dr. Cao has delivered over 200 invited research talks at major institutions including the Central Bank of Japan, Central Bank of Thailand, and U.S. Securities and Exchange Commission. He serves as Guest Associate Editor for Management Science and has co-chaired Review of Financial Studies conferences on FinTech and Machine Learning. His educational initiatives include a widely adopted free AI textbook for finance and accounting that has been implemented at universities worldwide including Indiana University, UT Dallas, and University of Minnesota. As Director of the AI Initiative for Capital Market Research, Dr. Cao leads a multidisciplinary team exploring practical AI applications in finance. The initiative has secured significant funding including a $150,000 grant from GRF CPAs & Advisors. His research group maintains strong industry connections through partnerships with regulatory bodies, financial institutions, and technology companies, facilitating the translation of academic research into practical financial applications.

Ihab IlyasView profile

Professor

• Data Management
• Data Cleaning
• Machine Learning for Data Quality
• +6 more

Professor Ihab Ilyas is a leading figure in data management and machine learning at the Cheriton School of Computer Science , University of Waterloo , where he holds the Thomson Reuters Research Chair in Data Quality . He is currently on leave from the university, serving as a Distinguished Engineer, Proactive Intelligence at Apple Inc . His research focuses on data cleaning , large-scale data integration , knowledge graphs , and machine learning applications in data quality . He has pioneered systems like HoloClean and Saga , with commercial impacts through co-founded startups Inductiv (acquired by Apple) and Tamr . PhD in Computer Science from Purdue University Co-founder of Inductiv (acquired by Apple) Co-founder of Tamr Research Interests include: Probabilistic and uncertain data management Machine learning for data quality and enrichment Big data systems and information extraction Knowledge graph construction and optimization Scientific Awards and Recognitions include: Fellow of the Royal Society of Canada (2024) C.C. Gotlieb Computer Award (2024) IEEE Fellow (2021) ACM Fellow (2020) Cheriton Faculty Fellowship (2013-2016) Ontario Early Researcher Award

Tatyana YordanovaView profile

Associate Professor

• Figure Skating
• Short Track Speed Skating
• Ice Dancing
• +7 more

Assoc. Prof. Tatyana Yordanova is affiliated with the National Academy of Sports in Bulgaria, where she serves in the Faculty of Sports and Department of Technical and Ice Sports . She holds the academic rank of Associate Professor and has been leading the department since 2024. Education: Master's from State Central Institute of Physical Culture (1987); Doctorate in Sports Science (2019) Languages: English, Italian, Russian Her research focuses on technical elements in figure skating , speed-power abilities , balance stability , and judging systems . She has extensively studied jump mechanics and anthropometric factors in skaters. Her publications from 2018-2024 include studies on judging systems, jump techniques, and sports management. Key themes in her work involve training methodology, competition analytics, and biomechanics of skating. Since 2002, she has worked as an international figure skating judge, including at the Sochi 2014 Olympics . She chaired the Bulgarian Skating Federation (2011-2023) and participated in projects like Erasmus+ mobility (2022, 2024). Her pedagogical career includes roles as scientific assistant (1988), senior assistant (2006), and associate professor (2023).

Asaf CidonView profile

Associate Professor

• Software Systems
• Storage Systems
• Machine Learning
• +4 more

Asaf Cidon is an Associate Professor at Columbia University, jointly affiliated with the Department of Electrical Engineering and Computer Science, and a member of the Data Science Institute. His research focuses on software systems , storage , large-scale machine learning , and cybersecurity . Stanford University - PhD in Electrical Engineering Stanford University - MS in Electrical Engineering Technion - BS in Computer and Software Engineering His work in distributed storage systems has been commercialized by companies such as Facebook, Tibco, and Rubrik. He has led projects like Sentinel and Forensics during his industry career. Recent publications highlight advancements in software-based radiation protection (ASPLOS'26), PCIe pooling with CXL (HotOS'25), and AI phishing detection (IMC'25). These reflect his expertise in system architecture , security , and networking . Scientific recognitions include best paper awards at OSDI, Usenix Security, CIDR, and ATC, along with NSF CAREER and ARO Young Investigator Awards . His papers Cookie Monster (SOSP'24) and Chablis (CIDR'24) received notable accolades. He has mentored numerous PhD and Master's students , including Edward Guo, Harry Wang, and Teng Jiang, many of whom now hold roles at Google, Meta, Amazon, and academic institutions. His lab at Columbia is actively recruiting CS and EE PhD students. Prior to Columbia, he founded and led the startup Sookasa to acquisition and served as Senior Vice President of Email Protection at Barracuda Networks , managing a $200M business with 100 engineers.

Daniel W. BlissView profile

Professor

• Radar
• Wireless Communications
• Precision Positioning
• +7 more

Daniel W. Bliss is a Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University and Director of ASU's Center for Wireless Information Systems and Computational Architectures (WISCA). With over $50 million in research funding as principal investigator from organizations including DARPA, ONR, Google, and Airbus, his work bridges theoretical foundations with practical implementations across multiple domains of wireless systems. Dr. Bliss received his educational foundation with a B.S.E.E. from Arizona State University (1989), followed by M.S. and Ph.D. degrees in Physics from the University of California-San Diego (1995, 1997). His academic journey includes significant industry experience at General Dynamics (1989-1993) and MIT Lincoln Laboratory (1997-2012) before joining ASU. His research program focuses on advanced wireless systems spanning radar, communications, precision positioning, computational architectures, and medical monitoring applications. Bliss employs information theory, estimation theory, and signal processing to develop novel system concepts with disruptive capabilities. Current research emphasizes RF convergence, integrated sensing and communications, and anticipatory medical analytics using wireless technologies, with particular focus on extracting physiological data from radar signals. Analysis of recent publications reveals a strong trend toward integrated sensing and communications systems, particularly utilizing mmWave and radar technologies for medical monitoring applications. His work increasingly bridges traditional communications and radar domains while expanding into physiological monitoring, demonstrating a clear trajectory toward convergence of wireless technologies for healthcare applications and remote vital sign detection. Dr. Bliss has received significant recognition for his contributions: Fellow of the IEEE (2015) 2021 IEEE Warren D. White Award for Excellence in Radar Engineering 2016-2017 Top 5% Teaching Award at ASU 2017 ASU Fulton Engineering Exemplar Faculty As a dedicated mentor, Dr. Bliss has supervised numerous graduate students through successful dissertation and thesis defenses across both PhD and Master's programs. His research portfolio includes substantial funding from diverse sources with over $50 million secured as principal investigator. Current projects include the $17M DARPA DASH project focused on advanced software-reconfigurable heterogeneous SoCs for next-generation RF systems, and multiple initiatives in contactless vital sign monitoring using radar technologies. Dr. Bliss leads the BLISS Lab and serves as director of WISCA, fostering interdisciplinary research in wireless systems. His team includes researchers working on distributed coherent systems, MIMO radar, RF convergence, and medical monitoring applications, with recent successes including the Making Waves team that tied for first place in the Air Force Spark Tank challenge. He has founded two startup companies: DASH Tech Integrated Circuits Company and the Big Little Sensor Company, focusing on high-performance embedded processing and small-scale radar physiological monitoring, respectively.

Wendy JuView profile

Associate Professor

• Interaction with Automation
• Interaction Design Research
• Human Robot Interaction
• +2 more

Wendy Ju is an Associate Professor of Information Science at Cornell Tech, with appointments in the Cornell Ann S. Bowers College of Computing and Information Science, the Jacobs Technion-Cornell Institute, and the Technion-Israel Institute of Technology. Previously, she served as executive director of interaction design research at Stanford University's Center for Design Research and as an associate professor of interaction design at the California College of the Arts. Her work bridges human-computer interaction, design, and robotics with a focus on how interactive devices can communicate with people without interrupting them. PhD in Mechanical Engineering from Stanford University Master's degree in Media Arts and Sciences from MIT Professor Ju's research centers on implicit interactions, human-robot collaboration, and automotive interfaces. She investigates how people interact with automated systems in natural contexts, develops methods for early-stage prototyping of autonomous technologies, and examines the social implications of robotics in urban environments. Her work spans from theoretical frameworks to practical applications, with particular emphasis on designing systems that integrate seamlessly into human activities without demanding constant attention. Her recent publications reveal a strong trajectory toward understanding human-robot interaction in public urban spaces, with increasing focus on robot navigation in city streets, the social implications of autonomous vehicles, and the integration of generative AI in design processes. Her work consistently bridges theoretical HCI frameworks with practical applications in transportation, urban design, and everyday robotics. Inducted into the ACM SIGCHI Academy (2025) Multiple Honorable Mention Awards at ACM CHI and DIS conferences Best Paper Award at Multimodal Technologies and Interaction (2023) Best Student Paper Award at IEEE Intelligent Vehicles Symposium (2017) Best Demonstration Award at HRI (2017) Professor Ju actively mentors numerous PhD and master's students, many of whom have become leading researchers in HCI and robotics. Her research has been supported by significant grants from NSF and industry partners, enabling extensive field studies of human-robot interaction in real-world settings. She has pioneered methodologies for studying autonomous vehicle interactions through both simulated and naturalistic driving environments. Her work with the Jacobs Technion-Cornell Institute supports interdisciplinary research at the intersection of computing, design, and urban technology. She has established research partnerships with transportation authorities, automotive companies, and urban planning organizations to study how emerging technologies can enhance urban mobility while respecting human needs and social contexts.

Sarah BillingtonView profile

Professor

• Sustainable Building Design
• Human Wellbeing in Built Environments
• Affordable Housing
• +3 more

Sarah Billington is the UPS Foundation Professor of Civil and Environmental Engineering at Stanford University and a Senior Fellow at the Woods Institute for the Environment. Her research program focuses on sustainable building design, human wellbeing, and ethical supply chain practices. Education: PhD, University of Texas at Austin, Structural Engineering (1997) MSE, University of Texas at Austin, Structural Engineering (1994) BSE, Princeton University, Civil Engineering & Operations Research (1990) Her lab employs interdisciplinary methods to study how built environments impact human physical, psychological, and social wellbeing. Current projects include developing tools to quantify nature exposure in buildings, exploring affordable housing's role in wellbeing, and using AI to assess forced labor risks in material supply chains. While no longer active in this area, her group previously pioneered research on sustainable construction materials like bio-based composites and ductile cement-based composites. Her recent publications address topics such as nature integration in architecture, workplace design optimization, and health impacts of building materials. The Billington Lab at Stanford fosters collaborative team science, welcoming partnerships to advance occupant-centric engineering solutions.

Clifford SteinView profile

Professor

• Algorithms
• Combinatorial Optimization
• Operations Research
• +4 more

Clifford Stein is a Professor of Industrial Engineering and Operations Research (IEOR) and Computer Science at Columbia University, and Associate Director for Research at the Data Science Institute. He holds a Ph.D. (1992), M.S. (1989), and B.S.E. (1987) from MIT and Princeton University, respectively. His research focuses on algorithms, combinatorial optimization, operations research, scheduling, and computational biology. A co-author of the best-selling textbook Introduction to Algorithms , Stein has published widely in top venues and holds prestigious awards like ACM Fellow and NSF Career Award. His work includes foundational contributions to minimum cut algorithms, scheduling theory, and network optimization, supported by NSF and Sloan Foundation grants. Stein has advised over 40 graduate and undergraduate students, many now in academia and industry.