新利18用户登录,新利18keno Skip to main content A. James Clark School of Engineering Contact Clark School Administration Give Ways to Give Contact Us About Giving Visit Apply Recruit Search this site Mobile Navigation Trigger Home Main Menu Mobile Navigation Trigger Reverse About Us Facts & Figures Who Was A. James Clark? 2020 Strategic Plan Meet the Dean Board of Visitors Building Together: An Investment for Maryland Diversity Recognition Clark School and UMD-Wide Honors Professional Track Faculty Excellence Award Invention of the Year Award Distinguished University Professor Distinguished Scholar-Teacher Award E. Robert Kent Outstanding Teaching Award for Junior Faculty Dr. Marilyn Berman Pollans Outstanding Service Award for Staff Newcomer of the Year Staff Award The Poole and Kent Teaching Award for Senior Faculty Faculty Service Award Humble Hero Staff Award Esprit de Corps Staff Award Junior Faculty Outstanding Research Award Senior Faculty Outstanding Research Award Dean’s Outstanding Performance Award for Professional Track Faculty Student Competition Advisor Award Terry Island Outstanding Advisor Award National Awards and Honors National Science Foundation CAREER Awards PECASE Awards Academics & Students Degree Programs Majors & Minors Online Learning Professional Programs Current Students Commencement Ceremony Student Competitions Alumni Cup Competition Clark School Three-Minute Thesis (3MT) Competition Dean&＃039;s List Engineering Career Services Engineering Honors Program Financial Aid Future Faculty Program Graduate Students Graduate Recruitment, Fellowship, and Scholarship Programs Graduate Fellowship/Scholarship Acceptance Form Societies & Clubs Study Abroad Undergraduate Awards Ceremony 2023 Clark School&＃039;s Honors & Awards 2024 Clark School&＃039;s Honors & Awards 2020 Clark School&＃039;s Honors & Awards 2021 Clark School&＃039;s Honors & Awards 2022 Clark School&＃039;s Honors & Awards Engineering Academic Services Undergraduate Students Prospective Students Freshmen Applicants Prepare Top 25 Engineering Source Schools 2019-2020 2020-2021 2018-2019 2017-2018 2016-2017 2015-2016 2014-2015 2013-2014 2012-2013 2011-2012 2010-2011 2009-2010 2021-22 2022-2023 2023-2024 Decide Degree Programs Information Sessions Clark School Ambassadors Nadeen Alomar Tanya Budhiraja Nathan Chandran Alessandra Contreras Kyla Erman Lauren Gomes Jillian Jacob Michael Ogunsemowo Pruthav Patel Jennifer Tartaglia Ankit Verghese Noah Wigglesworth Scholarships Societies & Clubs Research & Innovation Diversity Engineering Academic Services The First Year Graduate Applicants Transfer and Current UMD Student Applicants Contact Us Scholarships Scholarships Application Searchable Scholarships Database Irving & Ida Rabin Scholarship Engie Chuck Edwards Memorial Scholarships Clark Foundation Scholarships & Fellowships A. James Clark Scholars Program Clark Opportunity Transfer Scholars Program Clark Doctoral Fellows Program Clark Legacy Scholarships Clark Scholarship Programs Team Acceptance Instructions Thanking Your Scholarship Donor Emergency Funding & Basic Needs Resources Additional Funding Information K-12 Pre-College Programs School Year Programs Summer Programs Experience Engineering Virtual Summer Program Discovering Engineering Student Affairs & Academic Success Programs Center for Minorities in Science and Engineering About Us Visitor Information CMSE Advisory Board Admitted Students Student Programs & Support University of Maryland LSAMP Bridge Program for Scientists and Engineers Undergraduate Research Program STEM Program Bridge to the Doctorate Fellowship Program Alumni Registration Post-Baccalaureate Research Experiences for LSAMP Students (PRELS) Summer Internship with CMSE Engineering Student Societies Funding Request for Student Programs Winter Student Leadership Retreat AmazonNext Scholars Program Pre-College Programs ESTEEM/SER-Quest Summer Program Diversity in Engineering at UMD Events & Photos Annual Student Recognition Ceremony LSAMP Fall Research Symposium USM LSAMP Student Presentations Support CMSE The Path Forward Alumni Stories Remembering CMSE Alumni Alumni Events 40th Anniversary CMSE Alumni Testimonials Engineering Career Services About Us Peer Assistants For Students Career Exploration Career Fairs Career Resources & Handouts Diversity & Inclusion at Work Events Job Boards Information Sessions International Students Internships & Co-ops Internship/Co-op FAQs Job Update Form On-Campus Jobs Policies Recruiters in Residence Salaries & Employer Lists Summer Advice Workshops For Employers Career Fairs Co-op & Internship Programs Enrollment Data Hiring an International Student Information Sessions Policies Job Postings & Campus Interviews Recruiters in Residence Salary Information Student Societies Visitor Information Employment Outcomes Cybersecurity Employers Energy Systems Employers Project Management Employers Reliability Engineering Employers Robotics Employers Software Engineering Employers Systems Engineering Employers Technology Entrepreneurship and Corporate Innovation Employers Telecommunications Employers Employers for Aerospace Engineering Employers for Bioengineering Employers for Chemical Engineering Employers for Civil Engineering Employers for Computer Engineering Employers for Electrical Engineering Employers for Fire Protection Engineering Employers for Materials Engineering Employers for Mechanical Engineering Employers for Professional Master&＃039;s of Energy Systems Global Engineering Leadership Study Abroad Clark Abroad Fall 2021 Course Database Research and Internships Abroad Short-Term Study Abroad Study Abroad Contacts Study Abroad FAQ Clark in Madrid Aerospace Engineering Bioengineering Chemical and Biomolecular Engineering Civil Engineering Computer Engineering Electrical Engineering Fire Protection Engineering Materials Science and Engineering Mechanical Engineering Computer Science Clark in Madrid for the iSchool Clark in Prague Minor in Global Engineering Leadership Global Leadership Courses ENES317 Introduction to Engineering Leadership ENES424 Engineering Leadership Capstone ENES472 International Business Cultures ENES475/675 Leadership in Times of Crisis Visiting International Students Degree-Seeking Students Engineering Exchanges Resources Global Visitor Research Training Program Leadership & Service Contact Us Engineering Academic Services Academic Peer Coaching Program Academic Policies Academic Probation Contact an Advisor Course Permission for Non-Majors Engineering Honors Engineering Student Athletes Forms & Requests Four-Year Plans Graduation Minors Parent & Family Resources Registration Summer Engineering Orientation Transfer Current Transfer Students Internal Transfer Students External Transfer Students Contact Us Tutoring Resources Undecided Engineering Preparing for an Advising Appointment Exploring Engineering Majors Declaring an Engineering Major Women In Engineering About Us Facts & Figures Meet the WIE Staff Visitor Information Student Advisory Board Current Students Living & Learning Communities Flexus Virtus Flexus & Virtus Programming Board Peer Mentoring Program Summer Internship Positions Get Involved WIE Tutoring Services Technical Workshops Partnerships & Giving Future Students 6-12 Programs emPower Summer Program WIE Change the World: An Introduction to Engineering Admitted Students Prospective Students Superstar Spotlights Keystone Program About Faculty Courses Student Resources Teaching Fellows Program Keystone Center Alternative Proctoring Engineering Education Research Engineering Education Speakers Series Research & Innovation Innovation & Entrepreneurship Innovation Hall of Fame All Inductees White Symposium Featured Research Areas Featured Institutes and Centers Research Facilities Research Opportunities Alumni Update Your Information Alumni Network Board of Directors Alumni Photo Galleries Golden Terps Alumni Cup Become an Alumni Cup Sponsor Alumni Awards Glenn L. Martin Medal Greenaugh Award 125th Anniversay Medal Signature Events Early Career Distinguished Alumni Society ECDA Society – Class of 2022 Early Career Award 2014-2018 Industry Partner with the Clark School Corporate Partners Hire a Student News E@M Magazine E-Newsletter News Center Press Releases Events Whiting-Turner Business & Entrepreneurial Lectures Mpact Lecture Series Commencement Ceremony Commencement Livestream Digital Commencement Program Graduate Celebration Events Calendar Contact Clark School Administration Give Ways to Give Contact Us About Giving Visit Apply Recruit Directories Corporate Partners Careers Media Facilities ClarkNet Facebook Twitter Youtube Linkedin HomeFaculty DirectoryDutta, Sanghamitra Faculty Directory Dutta, Sanghamitra Assistant Professor Electrical and Computer Engineering 2115 AV Williams Building [email protected] (301)-405-2677 View CV Website(s): WebpageGoogle Scholar OVERVIEW RESEARCH INTERESTS TEACHING PUBLICATIONS RELATED NEWS EDUCATION Ph.D., Electrical and Computer Engineering, Carnegie Mellon University M.S., Electrical and Computer Engineering, Carnegie Mellon University B. Tech., Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur AWARDS 2024 NSF CAREER Award 2023 Northrop Grumman Seed Grant 2022 Simons Institute Fellowship for Causality 2021 A G Milnes Outstanding Thesis Award 2020 Cylab Presidential Fellowship 2019 K&L Gates Presidential Fellowship in Ethics and Computational Technologies 2019 Axel Berny Presidential Graduate Fellowship 2017 Tan Endowed Graduate Fellowship 2016 Prabhu and Poonam Goel Graduate Fellowship 2015 Nilanjan Ganguly Memorial Award for Best B. Tech. Thesis 2014 HONDA Young Engineer and Scientist Award ABOUT Sanghamitra Dutta is an assistant professor in the Department of Electrical and Computer Engineering at the University of Maryland College Park since Fall 2022. She is also affiliated with the Center for Machine Learning (CML) at UMIACS. Prior to joining UMD, she was a senior research associate at JPMorgan Chase AI Research New York in the Explainable AI Centre of Excellence (XAI CoE). She received her Ph.D. and Masters’s from Carnegie Mellon University and B. Tech. from IIT Kharagpur, all in Electrical and Computer Engineering. Her research interests broadly revolve around reliable and trustworthy machine learning. She is particularly interested in addressing the challenges concerning fairness, explainability, privacy, and reliability, by bringing in a novel foundational perspective deep-rooted in information theory, statistics, causality, and optimization. Her research has featured in New Scientist and Montreal AI Ethics Brief, and also been adopted as part of the fair-lending model review at JPMorgan. In her prior work, she has also examined problems in reliable computing, proposing novel algorithmic solutions for large-scale distributed machine learning, using tools from coding theory (an emerging area called “coded computing”). Her results on coded computing has received substantial attention from across disciplines. She is a recipient of the 2024 NSF CAREER Award, 2023 Northrop Grumman Seed Grant, 2022 Simons Institute Fellowship for Causality, 2021 AG Milnes Outstanding Thesis Award from CMU and 2019 K&L Gates Presidential Fellowship in Ethics and Computational Technologies. She has also pursued research internships at IBM Research and Dataminr. Her research vision is to build the foundations of reliable artificial intelligence (AI), beginning from a fundamental understanding of the challenges in reliability and trust, and carrying them all the way to practical implementations, so that AI can truly bring about social good. Her research interests include: Trustworthy Machine Learning Fairness and Explainability Information Theory Optimization, Statistics, Estimation Theory Causal Inference Coded Computing ENEE621: Detection and Estimation Theory Spring 2023 ENEE436: Foundations of Machine Learning Fall 2022, Fall 2023 2024 F. Hamman and S. Dutta, “Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition,” International Conference on Learning Representations (ICLR 2024). F. Hamman and S. Dutta, “A Unified View of Group Fairness Tradeoffs Using Partial Information Decomposition,” IEEE International Symposium on Information Theory (ISIT 2024). A. K. Veldanda, I. Brugere, S. Dutta, A. Mishler, and S. Garg. “Hyper-parameter Tuning for Fair Classification without Sensitive Attribute Access,” Transactions on Machine Learning Research (TMLR 2024). F. Hamman, E. Noorani, S. Mishra, D. Magazzeni, and S. Dutta, “Robust Algorithmic Recourse Under Model Multiplicity with Probabilistic Guarantees,” Journal on Selected Areas in Information Theory: Information-Theoretic Methods for Trustworthy Machine Learning (JSAIT 2024). 2023 F. Hamman, E. Noorani, S. Mishra, D. Magazzeni, and S. Dutta, “Robust Counterfactual Explanations for Neural Networks with Probabilistic Guarantees,” International Conference on Machine Learning (ICML 2023). F. Hamman and S. Dutta, “Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition,” ICML Workshop on Federated Learning and Analytics in Practice (ICML-FL Workshop 2023). S. Sharma, S. Dutta, E. Albini, F. Lecue, D. Magazzeni and M. Veloso, “REFRESH: Responsible and Efficient Feature Reselection guided by SHAP values,” AAAI/ACM Conference on AI, Ethics, and Society (AIES 2023). F. Hamman, J. Chen, and S. Dutta, “Can Querying for Bias Leak Protected Attributes? Achieving Privacy With Smooth Sensitivity,” ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT 2023). S. Garg, S. Dutta, M. Dalirrooyfard, A. Schneider, Y. Nevmyvaka, “In- or Out-of-Distribution Detection via Dual Divergence Estimation,” Conference on Uncertainty in Artificial Intelligence (UAI 2023). A. K. Veldanda, I. Brugere, J. Chen, S. Dutta, A. Mishler, and S. Garg, “Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale,” Transactions on Machine Learning Research (TMLR 2023). S. Dutta, F. Hamman, “A Review of Partial Information Decomposition in Algorithmic Fairness and Explainability,” Entropy 2023. 2022 S. Dutta, J Long, S Mishra, C Tilli, D Magazzeni, “Robust Counterfactual Explanations for Tree-Based Ensembles,” International Conference on Machine Learning (ICML 2022). P. Mathur, A T Neerkaje, M Chhibber, R Sawhney, F Guo, F Dernoncourt, S Dutta, D Manocha, “MONOPOLY: Financial Prediction from MONetary POLicY Conference Videos Using Multimodal Cues,” ACM Multimedia 2022 (ACM-MM 2022). S Dutta, P Venkatesh, P Grover, “Quantifying Feature Contributions to Overall Disparity Using Information Theory,” AAAI-22 Workshop on Information-Theoretic Methods for Causal Inference and Discovery (AAAI Workshop 2022). 2021 P Venkatesh, S Dutta*, N Mehta*, P Grover, “Can Information Flows Suggest Targets for Interventions in Neural Circuits?,” Neural Information Processing Systems (NeurIPS 2021). S. Dutta, P. Venkatesh, P. Mardziel, A. Datta and P. Grover, “Fairness under Feature Exemptions: Counterfactual and Observational Measures,” IEEE Transactions on Information Theory 2021. S. Dutta, J. Wang, and G. Joshi, “Slow and stale gradients can win the race,” IEEE Journal on Selected Areas in Information Theory 2021. S Mishra, S Dutta, J Long, D Magazzeni, “A Survey on the Robustness of Feature Importance and Counterfactual Explanations,” Explainable AI in Finance (XAI-FIN21). C. Jiang*, B. Wu*, S. Dutta and P. Grover, “Bursting the Bubbles: Debiasing Recommendation Systems While Allowing for Chosen Category Exemptions,” BIAS Workshop at ECIR (ECIR Workshop 2021). S. Dutta, L. Ma, T. K. Saha, D. Liu, J. Tetreault, and A. Jaimes, “GTN-ED: Event Detection Using Graph Transformer Networks,” TextGraphs Workshop at NAACL (NAACL Workshop 2021). 2020 S. Dutta, D. Wei, H. Yueksel, P. Y. Chen, S. Liu, and K. R. Varshney, “Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing,” International Conference on Machine Learning (ICML 2020). S. Dutta, P. Venkatesh, P. Mardziel, A. Datta and P. Grover, “An Information-Theoretic Quantification of Discrimination with Exempt Features,” AAAI Conference on Artificial Intelligence (AAAI 2020, ORAL). P. Venkatesh, S. Dutta, and P. Grover, “How else should we define Information Flow in Neural Circuits,” IEEE International Symposium on Information Theory (ISIT 2020). P. Venkatesh, S. Dutta, and P. Grover, “Information Flow in Computational Systems,” IEEE Transactions on Information Theory, Sep 2020. S. Dutta*, M. Fahim*, H. Jeong*, F. Haddadpour*, V. Cadambe, and P. Grover, “On the Optimal Recovery Threshold of Coded Matrix Multiplication,” IEEE Transactions on Information Theory, Jan 2020. S. Dutta*, H. Jeong*, Y. Yang*, V. Cadambe, T. M. Low and P. Grover, “Addressing Unreliability in Emerging Devices and Non-von Neumann Architectures Using Coded Computing,” Proceedings of the IEEE, April 2020. 2019 P. Venkatesh, S. Dutta and P. Grover, “How should we define Information Flow in Neural Circuits,” IEEE International Symposium on Information Theory (ISIT 2019). S. Dutta, V. Cadambe and P. Grover, “Short-Dot: Computing Large Linear Transforms Distributedly using Coded Short Dot Products,” IEEE Transactions on Information Theory, Oct 2019. S. Dutta, Z. Bai, T. M. Low and P. Grover, “CodeNet: Training Large Scale Neural Networks in Presence of Soft-Errors,” Coding Theory For Large-scale Machine Learning Workshop at ICML (CodML Workshop, ICML 2019, Spotlight). 2018 S. Dutta, G. Joshi, P. Dube, S. Ghosh, and P. Nagpurkar, “Slow and stale gradients can win the race: Error-Runtime trade-offs in Distributed SGD,” International Conference on Artificial Intelligence and Statistics (AISTATS 2018). U. Sheth, S. Dutta, M. Chaudhari, H. Jeong, Y. Yang, J. Kohonen, T. Roos, and P. Grover, “An Application of Storage-Optimal MatDot Codes for Coded Matrix Multiplication: Fast k-Nearest Neighbors Estimation,” IEEE International Conference on Big Data (IEEE BigData 2018). S. Dutta*, Z. Bai*, H. Jeong, T. M. Low, and P. Grover, “A Unified Coded Deep Neural Network Training Strategy based on Generalized PolyDot Codes,” IEEE International Symposium on Information Theory (ISIT 2018). 2017 S. Dutta, V. Cadambe and P. Grover, “Coded Convolution for parallel and distributed computing within a deadline,” IEEE International Symposium on Information Theory (ISIT 2017). M. Fahim*, H. Jeong*, F. Haddadpour, S. Dutta, V. Cadambe, and P. Grover, “On the Optimal Recovery Threshold of Coded Matrix Multiplication,” Communication, Control and Computing (Allerton 2017). 2016 S. Dutta, V. Cadambe and P. Grover, “Short-Dot: Computing Large Linear Transforms Distributedly using Coded Short Dot Products,” Neural Information Processing Systems (NeurIPS 2016). S. Dutta and P. Grover, “Adaptivity provably helps: Information-theoretic limits on l0 cost of non-adaptive sensing,” IEEE International Symposium on Information Theory (ISIT 2016). S. Dutta, Y. Yang, N. Wang, E. Pop, V. Cadambe and P. Grover, “Reliable Matrix Multiplication using Error-prone Dot-product Nanofunctions with an application to logistic regression” (SRC Techcon, 2016). 2015 S. Dutta and A. De, “Sparse UltraWideBand Radar Imaging in a Locally Adapting Matching Pursuit (LAMP) Framework,” IEEE International Radar Conference (RADAR 2015). Reflecting on a Successful 2023–24 Academic Year As we come to the close of another academic year, it is a time to celebrate the achievements of our faculty, staff, students—and especially our graduates! Dutta to receive NSF CAREER Award Will Study Fairness and Explainability in Machine Learning Using Information-Theoretic Methods Edit your faculty profile Directories Corporate Partners Careers Media Facilities ClarkNet Facebook Twitter Youtube Instagram Linkedin Privacy Policy Accessibility umd.edu © 2024 University of Maryland 新利18用户登录,新利18keno
