جست‌وجوی استاد و پژوهشگر

Jason Williamsمشاهده پروفایل

استادیار

• Information Systems
• Health Informatics
• Artificial Intelligence
• +۳ مورد دیگر

Jason Williams serves as an Assistant Professor at Augusta University with dual appointments in the James M. Hull College of Business and the School of Computer and Cyber Sciences, Department of Computer Science. His academic work integrates information systems, health informatics, and artificial intelligence to address critical challenges in healthcare security and community-based decision support. Dr. Williams holds two concurrent Ph.D. degrees from Kennesaw State University (2020) in Business/Office Automation/Technology/Data Entry and Management Information Systems, complemented by an MBA from Georgia Southern University (2007) and a BBA in Information Science (2003). His professional certifications include AU Online Teaching (2022) and Project Management Professional (PMP, 2017). Ph.D., Business/Office Automation/Technology/Data Entry, Kennesaw State University, 2020 Ph.D., Management Information Systems, Kennesaw State University, 2020 MBA, Business Administration and Management, Georgia Southern University, 2007 BBA, Information Science/Studies, Georgia Southern University, 2003 AU Online Teaching Certification, Augusta University Center for Instructional Innovation, 2022 PMP Certification, Project Management Institute, 2017 His research centers on multimodal analysis of emotional and informational dynamics within online health communities, with significant focus on pregnancy-related decision support systems and AI-driven health query processing. Methodologically, he combines deep learning with action research to develop security frameworks and optimize information quality heuristics in healthcare contexts. Williams' 2024-2025 publications reveal a cohesive research trajectory examining AI-human performance comparisons in health communication, emotional support mechanisms in digital communities, and security awareness program development. His work consistently bridges theoretical information systems frameworks with practical healthcare applications through multimodal analysis techniques. Details regarding Dr. Williams' graduate student mentorship and external research funding portfolios are not specified in the available documentation.

Joyce Nilandمشاهده پروفایل

استاد

• Biomedical Informatics
• Computational Statistics
• Oncology Research
• +۳ مورد دیگر

Joyce Niland, Ph.D., holds the Estelle & Edward Alexander Chair in Information Sciences and is Professor & Chair of the Department of Diabetes & Cancer Discovery Science at City of Hope . She also maintains a joint appointment as Clinical Professor of Preventive Medicine at the Keck School of Medicine of USC . Her interdisciplinary lab integrates biomedical informatics, computational statistics, and clinical research to advance diabetes and cancer studies. Education : Ph.D. in Biometry (1984), M.S. in Biometry (1979), M.S. in Physical Therapy (1978) from University of Southern California; B.A. in Human Biology from Stanford (1975) Her research focuses on statistical methodology for longitudinal studies , clinical trials design in oncology , and secondary use of clinical data via natural language processing (NLP) . Notable projects include NLP query portability across institutions and optical character recognition tools for clinical data abstraction. She has led the development of data repositories and decision support systems critical to City of Hope’s cancer and diabetes research infrastructure. Recent publications highlight her work in breast cancer recurrence prediction , ovarian cancer treatment , and islet transplantation for diabetes . The Niland Lab collaborates with researchers like Joo Y. Song (lymphoma genetics) and John Yim (breast surgery outcomes), while advancing methods for mining unstructured clinical data. Memberships : American Medical Informatics Association, International Statistical Institute, American Statistical Association, Society for Clinical Trials Her lab team includes staff scientists like Rebecca Ottesen and statistical programmers Julie Kilburn, focusing on clinical data repositories , biostatistical modeling , and informatics infrastructure for biomedical discovery.

• Secure Systems
• Operating Systems
• Distributed Systems
• +۵ مورد دیگر

Nickolai Zeldovich is the Joan and Irwin M. (1957) Jacobs Professor of Electrical Engineering and Computer Science at MIT and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). His research focuses on building practical secure systems through the integration of formal methods with real-world system design. His research interests span secure systems, operating systems, distributed systems, formal verification, and cryptography. Recent work emphasizes automated reasoning for distributed system liveness, leakage-free hardware security modules, and privacy-preserving web technologies. Projects include CryptDB (encrypted databases), FSCQ (verified file systems), Algorand (cryptocurrency), and Vuvuzela (private messaging). His publication trends show consistent output in formal verification of distributed systems (Shipwright, Grove), hardware security (K2 Architecture), and privacy technologies (Tiptoe). Recent work increasingly combines theoretical verification with practical implementation constraints. Best Paper Awards at ACM SOSP (2013, 2015, 2017) Sloan Research Fellowship (2010) NSF CAREER Award (2011) MIT EECS Jamieson Award for Excellence in Teaching (2024) ACM SIGOPS Mark Weiser Award (2017) Amazon Research Award (2023) He advises PhD students through the PDOS research group with active grants from Amazon, Google, NSF, and DARPA. His teaching includes core courses like Computer Systems Security and Operating System Engineering, emphasizing hands-on security analysis. The PDOS lab maintains strong industry connections with multiple startup spin-offs and regular technology transfer to production systems. He leads the PDOS (Parallel and Distributed Operating Systems) Research Group, which focuses on building verified, secure, and high-performance systems through close collaboration between theory and practice.

Arie van Deursenمشاهده پروفایل

استاد

• Software Engineering
• Software Testing
• Trustworthy AI
• +۵ مورد دیگر

Arie van Deursen is a Professor in Software Engineering at Delft University of Technology, where he is affiliated with the Department of Software Technology within the Faculty of Electrical Engineering, Mathematics, and Computer Science (EEMCS). He previously served as head of department from 2016-2023 and currently chairs the Software Engineering Research Group (SERG). He is also the scientific co-director of AI4SE, a five-year collaboration between JetBrains and TU Delft investigating novel uses of artificial intelligence in software engineering. His research interests span software engineering, software testing, trustworthy AI, language models for code, software architecture, human aspects of software engineering, and digitalization in the financial and public sector. His work often bridges academic research with practical industry applications, as evidenced by his co-founding of two companies: The Software Improvement Group (2000) and PerfectXL (2010/2013). Recent publications show a strong focus on large language models in software engineering contexts, with multiple 2024-2025 papers examining code generation quality, multilingual code comments, contamination-free code datasets, and license infringement concerns in LLM training data. His research demonstrates significant engagement with the challenges and opportunities presented by AI in software development. AIWare 2024 ACM SIGSOFT Distinguished Paper Award ASE 2024 ACM SIGSOFT Distinguished Paper Award CAIN 2023 Distinguished Paper Award CSCW 2015 ACM SIGCHI Best Paper Award EASE 2016 Best Paper Award Professor van Deursen has (co-)supervised 50 PhD students and 100 MSc students throughout his career. He serves on the Advisory Council on IT Assessment (AcICT) of the Dutch government (since 2022) and the Advisory Board of ING Bank Netherlands (since 2024). He was elected as a fellow of the Netherlands Academy of Engineering (NAE) in 2023. He leads the AI4SE lab, which hosts 10 PhD students, dozens of MSc and BSc students, and collaborates with many TU Delft faculty members and JetBrains specialists. The lab focuses on investigating novel uses of artificial intelligence in software engineering, with particular attention to practical applications and industry relevance.

Kexin Rongمشاهده پروفایل

استادیار

• Database systems
• Machine learning
• Data analytics
• +۳ مورد دیگر

Kexin Rong is an Assistant Professor in the School of Computer Science at Georgia Institute of Technology. She leads the D2I Lab, which is part of the Georgia Tech database group, and maintains an affiliation with VMware Research Group as an affiliated researcher. Dr. Rong received her Ph.D. in Computer Science from Stanford University in 2021 (advised by Peter Bailis and Philip Levis) and her B.S. in Computer Science from California Institute of Technology in 2015. Her academic journey includes recognition as an EECS Rising Star by UC Berkeley and an Honorable Mention for the SIGMOD Jim Gray Doctoral Dissertation Award. Her research focuses on democratizing data science through systems that simplify large-scale data analytics for non-experts. She synthesizes techniques from data management, machine learning, and human-computer interaction to improve both computational and human efficiency. Current research directions include analytics over unstructured data (Video Labeling with SketchQL and Image Understanding with VCR) and data infrastructure for AI (Data preprocessing with LOTUS and DiffPrep, Checkpointing for DL training with Inshrinkerator). Her recent publications show a strong trend toward building user-friendly systems that bridge complex data analytics and non-expert users, with significant impact demonstrated by adoption of systems like ASAP by industry players including Datadog and TimescaleDB for time series visualizations. Amazon Research Award recipient (2024) SIGMOD 2024 Distinguished PC Award Multiple NSF awards (2023-2024) Best Paper Finalist at IISWC 2024 Catherine M. and James E. Allchin Early Career Professorship Dr. Rong actively mentors students, with recent PhD graduates including Peng Li (VLDB'23 Best Paper Award winner), Hantian Zhang, and Renzhi Wu now at leading tech companies. She teaches core database courses including CS6400 Database Systems Concepts and Design and CS4440 Emerging Database Technologies. The D2I Lab under her direction is committed to providing an inclusive environment that welcomes applicants from diverse backgrounds, especially those underrepresented in computer science.

Roee Shragaمشاهده پروفایل

استادیار

• Data Integration
• Data Discovery
• Human-in-the-loop Machine Learning
• +۷ مورد دیگر

Roee Shraga is an Assistant Professor in the Computer Science department and Data Science program at Worcester Polytechnic Institute (WPI). He previously held a Postdoctoral Research Associate position at the Data Lab within Khoury College of Computer Sciences at Northeastern University in Boston, MA. Ph.D. in Data Science, Technion – Israel Institute of Technology (2020) M.E. in Data Science, Technion (2017) B.Sc. in Industrial Engineering and Management (Information Management Engineering), Technion (2015) Shraga’s research focuses on Data Integration , Data Discovery , Human-in-the-loop Machine Learning , Information Retrieval , and Large Language Models (LLMs) . His work bridges data management, machine learning, and human-computer interaction to improve data quality in AI systems. Recent publications include advancements in LLM-driven data integration , semantic table union search , and contrastive entity resolution . Key contributions include the Explain-Da-V framework for semantic data versioning and FlexER for intent-aware entity resolution. Scientific recognition includes: iiWAS 2024 best paper award TaDA@VLDB24 best paper award Council for Higher Education (VATAT) scholarship Leonard & Diane Sherman Interdisciplinary Fellowship (2017) Daniel Excellence Scholarship (2019) Miriam & Aaron Gutwirth Memorial Fellowship (2020) He leads NSF-funded research on Human-in-the-loop AI (CRII grant) and co-organizes the HILDA workshop. His teams have won multiple Outstanding MQP Awards for undergraduate research.

Xin Liمشاهده پروفایل

دانشیار

• Theory of Computation
• Randomness Extractors
• Pseudorandomness
• +۶ مورد دیگر

Xin Li is an Associate Professor in the Department of Computer Science at the Whiting School of Engineering, Johns Hopkins University. He is a member of the theory group and conducts research in the theory of computation, with a focus on randomness in computation, complexity theory, coding theory, and cryptography. He has advised several Ph.D. students and is actively involved in organizing seminars and serving on program committees of major theoretical computer science conferences. Ph.D., University of Texas at Austin, 2011 B.S. and M.S., Tsinghua University, Beijing, China His research interests lie broadly in theoretical computer science, particularly in pseudorandomness , randomness extractors , and explicit combinatorial constructions . He has made significant contributions to the construction of two-source extractors and non-malleable codes. His earlier work includes research in quantum computing and human-computer interaction. The most recent publications show a strong trend in coding theory (especially insertion-deletion codes), extractors (two-source, affine, non-malleable), and streaming algorithms. His work combines deep combinatorial insights with applications in cryptography and complexity theory. Scientific Awards and Honors: Simons Postdoctoral Fellowship NSF CAREER Award CCF-1845349 Invited to Theory of Computing Special Issue (RANDOM 2018) Invited to SICOMP Special Issue (FOCS 2013) SICOMP Special Issue (FOCS 2011) Advising and Grants: Xin Li has advised multiple Ph.D. students including Kuan Cheng, Zhengzhong Jin, Yu Zheng, Songtao Mao, and Yan Zhong. He has served as a postdoctoral mentor and research assistant supervisor. He is supported by several grants including NSF Award CCF-1617713, NSF CAREER Award CCF-1845349, and the Johns Hopkins Catalyst Award. He co-organizes the theory seminar at CS@JHU and has served on the program committees of STOC, RANDOM, and other top conferences. Labs and Teams: Xin Li is a member of the theory group at the Johns Hopkins Department of Computer Science, which focuses on foundational aspects of computation, algorithms, and complexity.

Aravinda Prasad Sistlaمشاهده پروفایل

استاد

• Formal Methods in Concurrent and Distributed Systems
• Model Checking
• Temporal Logic
• +۷ مورد دیگر

Aravinda Prasad Sistla is a Professor in the Department of Computer Science at the University of Illinois at Chicago. His research focuses on formal methods for verifying concurrent and distributed systems, security analysis, and database management systems. Education: Ph.D. in Computer Science/Applied Mathematics from Harvard University (1983) M.E. in Computer Science from Indian Institute of Science (1976) B.Tech. in Electronics and Communications Engineering from National Institute of Technology, Warangal (1974) Research Interests: He works on static and runtime verification of concurrent and distributed systems, including model checking for deterministic and probabilistic systems, symmetry reductions to address state explosion, and runtime fault detection using Hidden Markov Models. His security research involves model checking access control policies, verifying cryptographic protocols, and preventing confidential information leakage. Earlier work includes query processing in moving object, active, and textual databases.

Shiri Melumadمشاهده پروفایل

دانشیار

• Consumer Psychology of Technology
• New Media and User-Generated Content
• Digital Marketing
• +۳ مورد دیگر

Shiri Melumad serves as an Associate Professor of Marketing at the University of Pennsylvania's Wharton School, where she teaches Consumer Behavior, Marketing Management, and advanced doctoral seminars. Her academic work bridges marketing theory with real-world digital innovation, focusing on how technology reshapes consumer cognition and decision processes. Her educational foundation includes: Ph.D. and M.Phil. in Marketing from Columbia University's Graduate School of Business B.A. in Psychology from Columbia University Professor Melumad's research examines the psychological mechanisms underlying technology adoption, with particular emphasis on smartphone effects , voice interfaces , and metaverse environments . Her work reveals how digital tools alter emotional expression in user-generated content, distort news interpretation through auditory versus visual processing, and create new consumer vulnerabilities in self-disclosure. Current projects investigate neural brand homophily and the cognitive impacts of large language models versus traditional web search. Analysis of her publication trajectory shows consistent focus on human-technology interaction evolving from smartphone psychology (2017-2020) to voice interfaces (2021-2023) and emerging metaverse/LLM research (2023-2024). Her methodology blends experimental design with computational text analysis, yielding insights applicable to digital marketing strategy and consumer protection policy. Her scholarly impact is recognized through: Best Article Award, Journal of Consumer Research (2023) Weitz-Winer-O'Dell Award Finalist, Journal of Marketing Research (2024) Paul E. Green Award Finalist, Journal of Marketing Research (2019) Ferber Award Finalist, Journal of Consumer Research (2021) Best Paper Award, La Londe Conference (2019) As a dedicated mentor, she supervises doctoral students including Yoon Duk Kim, Eric Park, and Jiani Xue, with multiple co-authored publications in top journals. Her teaching portfolio spans undergraduate, MBA, and doctoral levels, featuring courses that integrate cutting-edge research with strategic marketing applications. While specific grant funding isn't detailed, her work demonstrates sustained research productivity through continuous publication in premier outlets like Journal of Consumer Research and Journal of Marketing. Professor Melumad collaborates across disciplines with psychologists, information systems researchers, and neuroscientists, exemplified by her work on neural brand homophily. Her future research agenda explores AI's impact on consumer learning and the psychological dynamics of immersive virtual environments.

Russell L. Finley, Jr.مشاهده پروفایل

استاد

• Protein-Protein Interaction Networks
• Gene Regulatory Networks
• Cell Cycle Regulation
• +۳ مورد دیگر

Russell L. Finley, Jr. is a Professor at Wayne State University, affiliated with the departments of Molecular Medicine and Genetics and Biochemistry, Microbiology, and Immunology. He is a member of the Karmanos Cancer Institute and serves as Division Director for Education and Graduate Officer for CMMG. PhD, SUNY Upstate Medical University (1990) Dr. Finley's research focuses on understanding regulatory networks that control biological processes and molecular mechanisms of cell division. His lab employs high-throughput technologies such as yeast two-hybrid systems, RNAi screens, and protein microarrays to map gene/protein interaction networks in Drosophila and bacterial pathogens. His recent publications (2024–2019) highlight work on: CHEK2 genetic variants in breast cancer Protein interactomes of pathogens (Campylobacter jejuni, Streptococcus pneumoniae, dengue virus) Novel cyclin proteins (Cyclin Y, Cyclin J) in cell cycle regulation Meta-interactome approaches to improve functional predictions Integration of Drosophila interactome and transcriptome data Dr. Finley contributes to systems biology and bioinformatics through algorithm development (e.g., TraM for graph matching) and interaction confidence scoring methods. His work bridges fundamental cell biology with translational applications in cancer and infectious disease research. Mentoring: Dr. Finley is accepting new Master's students for 2024/2025 but not new Ph.D. students. Labs and Teams: His research is conducted within the Karmanos Cancer Institute and the College of Medicine Microbiology and Immunology department at Wayne State University.

Brandon Fainمشاهده پروفایل

استادیار

• Algorithmic Fairness
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Brandon Fain is an Assistant Professor of the Practice in the Department of Computer Science at Duke University's Trinity College of Arts & Sciences, where he has served since 2020 after previously holding an Assistant Research Professor position from 2019-2020. His academic home is firmly within computer science, with a focus on bridging theoretical computer science with practical, socially conscious applications. He teaches core algorithms and machine learning courses while maintaining an active research program that intersects computer science, economics, and ethics. Dr. Fain received his educational training at Duke University, where he earned: Ph.D. in Computer Science (2019), with thesis "Algorithms for Public Decision Making" (winner of outstanding dissertation award) M.S. in Computer Science (2018) B.A. in Physics with double major in Mathematics and minor in Computer Science from Transylvania University (2014, Summa Cum Laude) Dr. Fain's research focuses on developing algorithms that address fairness, ethics, and human compatibility in artificial intelligence systems. His work spans algorithmic fairness in multi-objective reinforcement learning, fair resource allocation, and ethical considerations in AI development. He approaches these challenges through the lens of computational economics, algorithmic game theory, and multi-agent systems, seeking to build AI that better aligns with human values and societal needs. His research is deeply motivated by a desire to create more humane algorithmic systems that can respond to complex human preferences in rigorous and equitable ways. Analysis of Dr. Fain's publication record reveals a consistent focus on fairness in computational systems, with particular emphasis on multi-objective decision making and ethical considerations in AI. His recent work shows increasing sophistication in handling nonlinear preferences and uncertainty in reinforcement learning contexts. A notable trend is his growing interest in the intersection of algorithmic fairness with political and social applications, as evidenced by his work on gerrymandering detection and participatory budgeting. His publications consistently appear in top venues for AI, algorithms, and computational social choice. Dr. Fain has received multiple teaching honors including: Top 5% Course Evaluations in Trinity College (Fall 2019) Dean's Award for Excellence in Teaching Certificate in College Teaching Preparing Future Faculty Fellowship Outstanding Ph.D. dissertation award (2019) Dr. Fain is deeply committed to undergraduate research mentorship, having guided numerous students to publish in top computer science venues. His CS+ Summer Undergraduate Projects program provides Duke undergraduates with immersive research experiences. His current grant portfolio includes significant NSF funding: NSF INCLUDES Alliance: The Alliance for Identity-Inclusive Computing Education (2021-2027) CUE: Collaborative Research: Effective Peer Teaching Across Computing Pathways (2020-2023) These grants reflect his commitment to broadening participation in computing and improving computer science education. Dr. Fain directs the CS+ Summer Undergraduate Projects in Computer Science program, which provides Duke undergraduates with intensive research experiences in teams of 3-4 students mentored by faculty and graduate students. He also actively collaborates with colleagues through programs like Data+ and Code+, creating a vibrant ecosystem for undergraduate research in computer science at Duke.

Venkat Arunمشاهده پروفایل

استادیار

• Networked systems
• Formal methods
• Internet congestion control
• +۴ مورد دیگر

Venkat Arun is an Assistant Professor in the Department of Computer Science at the University of Texas at Austin, College of Natural Sciences. His research focuses on making networked systems robust and performant through the application of formal methods. Previously, he completed his PhD at MIT and undergraduate studies at IIT Guwahati. He co-leads the UT Networked Systems Lab (UTNS) and has developed algorithms deployed at Meta. His research interests center around networked systems and formal methods, with a focus on developing conceptual, mathematical, and automated tools to design provably performant networked systems. Arun's work spans internet congestion control, video streaming, privacy-preserving computation, wireless networks, and mobile systems. A common theme across his research is using theoretical ideas to gain insights into real-world systems that would be difficult to discover otherwise. His recent publications demonstrate a strong focus on performance verification and synthesis for networked systems, with significant contributions to congestion control algorithms. His work bridges theoretical foundations with practical implementations, as evidenced by the deployment of his algorithms at Meta. The research spans formal verification techniques, network protocol design, and performance analysis across various networked applications. Scientific Awards: MIT EECS G. M. Sprowls PhD Thesis Award in Computer Science (2024) ACM SIGCOMM Doctoral Dissertation Award Runner-Up (2024) Marconi Society Young Scholar Award (2023) ACM SIGCOMM best student paper award (2022) ACM SIGCOMM best paper award (2017) MIT Jacobs Presidential Fellowship (2017) President of India Gold Medal - IIT Guwahati (2017) KVPY Government of India Scholarship (2013) Venkat Arun currently advises three PhD students: Haoyu Li (co-advised with Aditya Akella), Tony (Jia) Pan (co-advised with Isil Dillig), and Saarth Deshpande (co-advised with Neeraja Yadwadkar). His research group is funded by NSF grants including "Nets: Medium: An End-To-End Framework For Network" (2024) and "FMitF: Performance Verification for Networked Systems" (2024), as well as a generous gift from Mibura (2024). The group is managed by Destiny Turner. He leads several major research projects including Performance Verification and Synthesis, "Solving" Congestion Control, and Metasurfaces with Thousands of Antennas. Arun teaches CS 395T: Performance Analysis of Networked Systems and CS 356: Computer Networks at UT Austin.

Adam Klivansمشاهده پروفایل

استاد

• Machine Learning
• Theoretical Computer Science
• Learning Theory
• +۵ مورد دیگر

Adam Klivans is a Professor at the University of Texas at Austin's Department of Computer Science and serves as Director of the Institute for Foundations of Machine Learning (IFML) and the Machine Learning Lab. His work bridges machine learning and theoretical computer science, focusing on learning theory, computational complexity, and Gaussian space analysis. He has made foundational contributions to robust learning, distribution shift, and neural network trainability. Research Interests: Machine learning algorithms Computational complexity Robust learning under noise Distribution shift Gaussian space analysis Protein engineering Selected Publications Analysis (2023-2025): Foundational TDS learning framework for distribution shift Polynomial-time algorithms for halfspace and ReLU regression Key results on SQ lower bounds for neural networks Advances in list-decodable regression and outlier-robust learning Novel approaches to protein stability prediction Applications to Ising models and diffusion models Scientific Honors: NSF CAREER Award (2007) Microsoft Azure Data Science Initiative Award (2017) Best Student Paper COLT (2006) Best Paper Award at COLT (2024) Spotlight Presentations at NeurIPS (2019) Long-Term Participant Simons Institute (2018) Academic Leadership: Director, Institute for Foundations of Machine Learning Director, UT Machine Learning Lab Editorial Board, Theory of Computing Journal Mentoring current students: Kostas Stavropoulos, Gautam Chandrasekaran, Kulin Shah Alumni advisees: Surbhi Goel, Pravesh Kothari, Sushrut Karmalkar

Kathleen McKeownمشاهده پروفایل

استاد

• Natural Language Processing
• Text Summarization
• Natural Language Generation
• +۷ مورد دیگر

Kathleen McKeown is the Henry and Gertrude Rothschild Professor of Computer Science at Columbia University , where she has been a faculty member since 1982. She served as Founding Director of the Data Science Institute (2012–2017), Department Chair (1998–2003), and Vice Dean for Research (2003–2005) at the School of Engineering and Applied Science . Education : PhD in Computer Science and Information Science (1982) and MS in Computer and Information Science (1979) from the University of Pennsylvania , AB in Comparative Literature (1976) from Brown University Her research focuses on Natural Language Processing , Text Summarization , and Natural Language Generation , with applications in Social Media Analysis , Disaster Response , and Energy Behavior Messaging . She has pioneered neural methods for summarization, sentiment analysis in low-resource languages, and multi-media explanations. Her work includes 15 recent publications (2011–2013) on topics like discourse relation disambiguation , MT error correction , influencer detection , and hierarchical web summarization , reflecting trends in deep learning , cross-lingual NLP , and social media analytics . Honors & Awards : IEEE Innovations in Societal Infrastructure Award (2023) AAAI Fellow (1994) ACM Fellow (2003) Founding ACL Fellow (2012) Anita Borg Women of Vision Award (2010) NSF Presidential Young Investigator (1985) She leads the NLP Group at Columbia, focusing on real-time disaster updates , behavioral NLP , and sentiment analysis for marginalized communities. Her career spans leadership roles , grant-funded research , and foundational contributions to text generation and summarization .

Thodoris Lykourisمشاهده پروفایل

استادیار

• Machine Learning
• Dynamic Optimization
• Economics
• +۴ مورد دیگر

Thodoris Lykouris is an Assistant Professor of Operations Management at the MIT Sloan School of Management, affiliated with the Operations Management group and the Operations Research Center. His research focuses on data-driven sequential decision-making and spans across the areas of machine learning, dynamic optimization, and economics. Dr. Lykouris received his PhD in Computer Science from Cornell University, where he was advised by Éva Tardos. Prior to his current position at MIT, he was a postdoctoral researcher at Microsoft Research NYC. He holds a diploma in Electrical and Computer Engineering from National Technical University of Athens (Greece). His research interests include: Data-driven sequential decision-making Machine learning for operations Dynamic optimization Economics of decision-making Queueing theory and resource allocation Multi-agent learning systems Dr. Lykouris's work spans across several domains with applications in shared mobility systems, content moderation, refugee resettlement, and review systems. His research combines theoretical foundations with practical applications, often developing algorithms with provable guarantees. His scientific awards include: Google PhD Fellowship Cornell University Fellowship Finalist for George B. Dantzig Dissertation Award (2020) Finalist in INFORMS Junior Faculty Interest Group paper competition Finalist in INFORMS Nicholson Student Paper Competition (2018) Finalist in Applied Probability Society Student Paper Competition (2022) Dr. Lykouris currently serves as an Associate Editor in the Stochastic Models area of Operations Research and on the Executive Committee of Learning Theory Alliance. He has organized multiple workshops and programs, including an interdisciplinary semester-long program on Data-driven decision processes at the Simons Institute in 2022. He teaches courses including Introduction to Operations Management, Theory of Operations Management, and Hands-on Deep Learning at MIT.