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Malgorzata Agnieszka CyndeckaView profile

Professor

• EU/EEA Law
• State Aid Law
• Data Protection Law
• +14 more

Malgorzata Agnieszka Cyndecka is a Professor at the Faculty of Law, University of Bergen (UiB) , where she specializes in EU/EEA state aid law and data protection/GDPR. She is affiliated with SLATE (Centre for the Science of Learning & Technology) and serves as a member of the Norwegian Data Protection Board. Since 2019, she has been Associate Editor of the European State Aid Law Quarterly . She also holds an Associate Professor II position at the University of Oslo and contributes to interdisciplinary research on AI, privacy, and education. University: University of Bergen School: Faculty of Law Academic Rank: Professor Email: malgorzata.cyndecka@uib.no Affiliations: SLATE, Norwegian Data Protection Board, Council of Europe Expert Group on AI and Education Her research centers on EU/EEA state aid rules —particularly their application in tax, energy, and education sectors—and data protection law , with a focus on GDPR compliance in AI-driven educational technologies. She has led and contributed to major projects such as the Norwegian Data Protection Authority’s Sandbox for Responsible AI (AVT project), where she provided legal guidance on processing student data, and UiB’s DIGI courses, where she co-developed DIGI113 on Privacy and GDPR. Her work bridges legal theory with practical policy, influencing national and international frameworks on digital rights and public aid. The analysis of her recent publications reveals a strong focus on the evolution of state aid jurisprudence , especially the Market Economy Operator Principle (MEOP), burden of proof in aid cases, and sustainability in public support. Concurrently, her interdisciplinary work explores AI and privacy challenges in education , anonymization of unstructured data under GDPR, and ethical implications of algorithmic decision-making. Her contributions span legal doctrine, policy recommendations, and public commentary. Scientific Awards: European State Aid Law Quarterly PhD Award (2012–2016) for best doctoral dissertation in state aid law Advising and Grants: She supervises master’s students in EU/EEA law, data protection, and GDPR. She has been involved in externally funded projects including the Norwegian Data Protection Authority’s Sandbox for Responsible AI, the ENDO4P project (aimed at personalized endocrinology treatment via EU Horizon funding), and the Clean Up Project (Machine Learning for Anonymisation of Unstructured Personal Data) at the University of Oslo. She has also coordinated collaborations with Media City Bergen for law and technology education. Her teaching includes course leadership in JUS2302, JUS3502, JUS2303, JUS3503, and DIGI113. Labs and Teams: She is a key member of SLATE (Centre for the Science of Learning & Technology) at UiB and participates in multiple research groups including the Research Group for Information and Innovation Law. She contributes to interdisciplinary teams working on digital competence, AI ethics, and data governance in education and health. She is also active in the Academy for Young Researchers (AYF) and leads the EU and EEA Law Issues Committee in the Norwegian branch of the International Commission of Jurists (ICJ).

• Autism Spectrum Disorders
• Child Psychiatry
• Developmental Disabilities
• +8 more

Katarzyna Chawarska is the Emily Fraser Beede Professor of Child Psychiatry at Yale School of Medicine, with primary affiliation in the Child Study Center and secondary appointments in Pediatrics and Statistics. She is a leading expert in autism spectrum disorders (ASD), directing the NIH Autism Center of Excellence, the Social and Affective Neuroscience of Autism Program, and the Yale Toddler Developmental Disabilities Clinic. Education: PhD in Psychology, Yale University (2000) Post-Doctoral Fellowship, Yale University School of Medicine (2000) MS in Psychology, Yale University MPhil in Psychology, Yale University MA, Jagiellonian University (1986) Her research focuses on identifying early diagnostic markers and novel treatment targets in ASD, particularly in infants at risk due to familial, genetic, or perinatal factors. Her work integrates clinical assessment, neuroimaging, eye-tracking, and longitudinal design to understand the neurodevelopmental trajectories of autism. Her recent publications explore disrupted functional connectivity, atypical visual attention, social anhedonia, and familial recurrence in autism. She employs advanced methodologies including fMRI, eye movement dynamics, and machine learning to identify biomarkers. Her research spans developmental neuroscience, clinical psychology, genetics, and pediatric psychiatry, with strong emphasis on early detection and intervention. Scientific Awards: No specific awards mentioned in the provided text. Dr. Chawarska is the Principal Investigator on active clinical trials, including studies on emotional development in infants at risk for ASD and regulation of visual attention and emotion in autism. She mentors research through her lab and collaborates extensively with experts such as Fred Volkmar, James McPartland, and Frederick Shic. She leads the Chawarska Lab, which is part of the Center for Brain & Mind Health and the Wu Tsai Institute at Yale.

Jung Soo LimView profile

Assistant Professor

• Internet of Things
• Cyber-Physical Systems
• Wireless Networking
• +6 more

Jung Soo Lim is an Assistant Professor in the Department of Computer Science at California State University, Los Angeles, within the College of Engineering, Computer Science, and Technology. He earned his B.S. from Cal State LA and M.S. and Ph.D. from UCLA, returning to his alma mater as a part-time lecturer in 2014 before transitioning to a full-time assistant professor role in 2019. Education: B.S. (Cal State LA), M.S. (UCLA), Ph.D. (UCLA) His research focuses on Internet of Things (IoT) , Cyber-Physical Systems , Wireless Networking , Software Engineering , and Medical Computing . He has active projects in IoT applications for healthcare, urban safety, and wastewater monitoring, including the Center for Inclusive Computing (CIC) Transfer Pathways Project. Recent publications highlight interdisciplinary work bridging IoT, healthcare diagnostics, and smart city infrastructure. Notable topics include stroke detection algorithms , IoT communication protocols , and sensor-based environmental monitoring . Scientific Awards: Outstanding Senior at Cal State LA (1997) He teaches core computer science courses such as Computer Programming Fundamentals and Analysis of Algorithms, and actively mentors graduate students. His lab focuses on developing embedded systems and IoT solutions for real-world challenges.

Mike RosulekView profile

Professor

• Cryptography
• Secure Multi-Party Computation
• Private Set Intersection
• +3 more

Mike Rosulek is a Professor at Oregon State University's School of Electrical Engineering and Computer Science. He is a leading expert in cryptographic protocols for secure computation, with a focus on practical applications of garbled circuits, oblivious transfer, and private set intersection. His work bridges theoretical and applied cryptography, and he has authored the textbook The Joy of Cryptography , set for a revised print edition by MIT Press in December 2025. Research Interests Secure Multi-Party Computation Garbled Circuit Optimization Private Set Intersection Malicious Adversary Resistance Cryptographic Primitives Post-Quantum Security Rosulek has mentored numerous PhD and Master's students, including Ni Trieu , Lawrence Roy , and Jaspal Singh . His research is supported by grants from the NSF, Google, and Visa Research. Notable achievements include best paper honorable mentions at Crypto 2021 and the development of the first practical garbled neural networks. Scientific Awards NSF CAREER Award Google Faculty Research Award Visa Research Faculty Award Rosulek actively contributes to the cryptographic community through program committee roles at top conferences and co-organizing workshops like CFAIL. He also maintains an annotated bibliography of secure computation research and provides mentorship materials for graduate students.

Cristina Nita-RotaruView profile

Professor

• Cybersecurity
• Distributed Systems
• Computer Networks
• +5 more

Cristina Nita-Rotaru is a tenured Professor of Computer Science at Northeastern University's Khoury College of Computer Sciences , where she leads the Network and Distributed Systems Security Laboratory (NDS2) and is a founding member of the Cybersecurity and Privacy Institute . Previously, she was a faculty member at Purdue University from 2003 to 2015. Education: Ph.D. in Computer Science from Johns Hopkins University M.S. in Computer Science from Politehnica University of Bucharest , Romania Research Focus: Her research lies at the intersection of cybersecurity , distributed systems , and computer networks . She designs and builds resilient distributed systems and network protocols that maintain security, availability, and performance despite faults, misconfigurations, and attacks. Her work integrates formal methods , adversarial testing , blockchain security , and trustworthy AI . Funding & Impact: Her research has been supported by NSF , DARPA , ONR , Google , Ethereum Foundation , and others. She has received numerous awards, including the NSF CAREER Award (2006) and multiple best paper awards at top venues like NDSS , ACM CCS , and IEEE S&P . Scientific Awards: NSF CAREER Award (2006) NETYS 2023 Best Paper Award ACM SACMAT 2022 Best Paper & Test-of-Time Awards IEEE SafeThings 2019 Best Paper Award NDSS 2018 Best Paper Award ISSRE 2017 Best Paper Award DSN 2015 Best Paper Award IETF/IRTF Applied Networking Research Prize (2016, 2018, 2025) Purdue College of Science Research Award (2013) Purdue Excellence in Research Award (2012) Purdue College of Science Leadership Award (2012) Purdue College of Science Undergraduate Advising Award (2008) Purdue Teaching for Tomorrow Award (2007) Advising & Students: She has advised over 30 PhD and MS students at Northeastern and Purdue. Notable former students include Reza Curtmola (NJIT Professor) , Endadul Hoque (Syracuse University Assistant Professor) , and Max von Hippel (Bencify founding partner) . Labs & Teams: She directs the NDS2 Lab , focusing on network and distributed systems security, with projects spanning blockchain protocols , SDN security , IoT and connected cars , and formal verification of protocols .

Alex John LondonView profile

Professor

• Ethics of Artificial Intelligence
• Bioethics
• Research Ethics
• +5 more

Alex John London is the K&L Gates Professor of Ethics and Computational Technologies at Carnegie Mellon University, where he also serves as co-lead of the K&L Gates Initiative in Ethics and Computational Technologies, Director of the Center for Ethics and Policy, and Chief Ethicist at the Block Center for Technology and Society. His work spans multiple institutions, including affiliations with the Center for Bioethics and Health Law at the University of Pittsburgh. Dr. London earned his Ph.D. in Philosophy from the University of Virginia, followed by a post-doctoral fellowship at the University of Minnesota's Center for Bioethics. He joined Carnegie Mellon University in 2000 and has established himself as a leading scholar in ethics at the intersection of technology, medicine, and policy. His academic journey includes being a Visiting Scholar at Harvard University's Program in Ethics and Health. Professor London's research spans ethical and policy issues surrounding novel technologies in medicine, biotechnology and artificial intelligence, methodological issues in theoretical and practical ethics, and cross-national issues of justice and fairness. His work in AI ethics critically examines structural obstacles to safe and effective technologies, challenges conventional notions of algorithmic bias, and questions requirements for explainability in medical contexts. His foundational work on clinical equipoise and the 'integrative approach' to risk assessment has shaped research ethics guidelines globally. He has made significant contributions to international research ethics, particularly regarding justice, responsiveness to host community health needs, and post-trial access. Professor London's scholarly output demonstrates a trajectory toward addressing the ethical challenges of emerging technologies, with increasing focus on justice-led approaches to AI innovation, accountability frameworks, and the sociotechnical dimensions of healthcare AI systems. His work increasingly addresses how AI can be designed to respect human dignity while navigating complex ethical terrain in healthcare settings. New Directions Fellowship from the Andrew W. Mellon Foundation (2005, 2010) Hastings Center Fellow (2011) Elliott Dunlap Smith Award for Distinguished Teaching (2016) Distinguished Service Award from the American Society of Bioethics and Humanities (2017) As an educator, Professor London teaches courses on ethical theory, bioethics, ethics and AI, and research ethics. His influential textbook 'Ethical Issues in Modern Medicine' (8th edition) is one of the most widely used resources in medical ethics education. His book 'For the Common Good: Philosophical Foundations of Research Ethics' (2022) provides a comprehensive framework for understanding research ethics as serving the common good. Professor London has advised numerous students and mentored early-career researchers in bioethics and technology ethics. His policy work extends to multiple national and international organizations including the World Health Organization Expert Group on Ethics and Governance of AI, the National Academy of Medicine Action Collaborative, and the U.S. National Science Advisory Board for Biosecurity. Professor London leads several significant research initiatives, including serving as co-leader of the ethics core for the NSF AI Institute for Collaborative Assistance and Responsive Interaction for Networked Groups (AI-CARING). His Center for Ethics and Policy at CMU serves as a hub for interdisciplinary research addressing pressing ethical challenges in technology and healthcare. He is actively involved in shaping policy through his membership on the steering committee of the AAAI/ACM Conference on Artificial Intelligence, Ethics and Society (AIES) and his co-chair role in the U.S. National Academies planning committee on computational modeling of biological agents.

• Adaptive nonparametric statistics
• High-dimensional data analysis
• Financial time series modeling
• +5 more

Prof. Vladimir Spokoiny is a leading figure in stochastic algorithms and nonparametric statistics at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) and Humboldt University of Berlin . His work bridges mathematical statistics with practical applications in finance, medicine, and machine learning. Born in 1959 in Moscow, USSR PhD from Lomonosov Moscow State University (1988) Habilitation from Humboldt University (1996) Head of WIAS research group since 2000 Professor at Humboldt University since 2002 Spokoiny's research focuses on adaptive nonparametric methods, high-dimensional data analysis, and statistical finance. His innovations in local homogeneity testing and propagation-separation methods have advanced volatility modeling, image analysis, and manifold learning. He employs Bayesian optimization frameworks and stochastic control techniques for financial instrument pricing. Recent scientific contributions include generalized bootstrap procedures for Bures-Wasserstein barycenters (2024), dimension-free Laplace approximation bounds (2023), and structure-adaptive manifold estimation (2022). His 19+ PhD students and editorial roles in top journals like The Annals of Statistics demonstrate sustained academic impact. International Statistical Institute member American Statistical Association fellow Institute of Mathematical Statistics member Bernoulli Society member

• Statistical Machine Learning
• Algorithmic Statistics
• Robust Statistics
• +11 more

Sivaraman Balakrishnan is a Professor at Carnegie Mellon University with joint appointments in the Department of Statistics and Data Science and the Machine Learning Department. His research bridges statistical machine learning, algorithmic statistics, and robust inference. Education: Ph.D. in Computer Science from Carnegie Mellon University (Language Technologies Institute, advised by Jaime Carbonell); postdoctoral work at UC Berkeley (Department of Statistics, advised by Martin Wainwright and Bin Yu). Research Interests: Spanning robust statistics, domain adaptation, minimax hypothesis testing, assumption-light inference, causal inference, statistical optimal transport, non-parametric statistics, ranking, crowdsourcing, optimization, and topological data analysis. Key Research Trends: Recent work focuses on domain adaptation under label/misingness shifts, robust gradient estimation, smooth optimal transport maps, and conditional independence testing. He explores minimax optimal methods, univariate mean estimation, and high-dimensional regression with missing data. Scientific Awards: IMS Lawrence D. Brown Student Award (2021, 2020) NVIDIA Pioneer Award (2018) Franklin V. Taylor Memorial Best Paper Award (2018) Grants and Editorial Roles: NSF grants (CCF-1763734, DMS-1713003, DMS-2113684, DMS-2310632), Amazon Research Award (2021), Google Research Scholar Award (2021). Associate Editor for JASA and JRSSB ; Editorial Board member for Foundations and Trends in Statistics . Collaborative Groups: Co-organizes the Statistics and Machine Learning Reading Group and participates in the Causal Inference Working Group at CMU.

Jeff ZhangView profile

Assistant Professor

• Deep learning
• Computer architecture
• Embedded systems
• +5 more

Jeff Zhang is an Assistant Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University. He joined ASU in January 2023 after completing a postdoctoral fellowship at Harvard University. His research spans deep learning, computer architecture, embedded systems, and EDA, with particular emphasis on energy-efficient and fault-tolerant design for AI/ML systems and hardware accelerators. Education: Ph.D., New York University M.Eng., B.Eng., Hunan University Dr. Zhang's research bridges theoretical machine learning with practical hardware implementation, developing novel architectures that optimize performance, power consumption, and reliability. He has pioneered approaches for hardware acceleration of large language models, efficient sparse matrix operations, and novel memory technologies for AI workloads. His work has received multiple awards including IEEE Top Picks in Test and Reliability (2023) and IEEE Micro Best Paper Award (2022). His recent publications demonstrate a strong trend toward heterogeneous computing, with significant work in chiplet-based AI accelerators, photonic computing for AI, and 2.5D/3D integration techniques. The research spans from high-level compiler frameworks to circuit-level innovations, with a consistent theme of co-designing algorithms and hardware for optimal AI performance. His work on the SODA toolchain has been particularly influential in bridging Python to silicon. Scientific Awards: IEEE Top Picks in Test and Reliability, IEEE ITC, 2023 Best Paper Award, IEEE Micro, 2022 Best Paper Award Candidate, IEEE DATE, 2022 Best Presentation Award Nomination, ACM SIGDA DATE PhD Forum, 2020 Best Paper Award Nomination, IEEE VLSI Test Symposium, 2018 Ernst Weber Ph.D. Fellowship, New York University, 2015, 2016 Dr. Zhang actively mentors a diverse group of graduate and undergraduate students, with several alumni now working at leading technology companies including Apple, TSMC, and Ansys. His research is supported by prestigious grants from NSF, Sandia National Labs, and industry partners. He serves on technical program committees of numerous top conferences and has organized special sessions on emerging topics like Gen AI for Chip Design and LLM-Aided Design. Dr. Zhang leads a vibrant research group that collaborates extensively with industry partners and national laboratories. Current projects focus on next-generation AI hardware, including chiplet-based systems, photonic accelerators, and novel memory technologies for large language models. His group has developed several open-source tools and frameworks, including the SODA toolchain for bridging Python to silicon.

Abolfazl AsudehView profile

Associate Professor

• Algorithm Design
• Data Science
• Artificial Intelligence
• +4 more

Abolfazl Asudeh is an Associate Professor in the Department of Computer Science at the University of Illinois Chicago and director of the Innovative Data Exploration Laboratory (InDeX Lab) . He is a Senior Member of ACM and IEEE , serving as Associate Editor for IEEE Transactions on Knowledge and Data Engineering , VLDB Ambassador , and VLDB Endowment Liaison to NSF . His research focuses on Algorithm Design for Data and AI problems , emphasizing efficient, accurate, and responsible solutions through Approximation Algorithms , Randomized Methods , and Computational Geometry . Recent work explores LLM optimization ( Needle ), fair data structures ( FairHash ), and responsible AI frameworks ( Chameleon ). Scientific awards include Communications of the ACM Research Highlight Google Research Scholar Award SIGMOD 2019 Research Highlight Best of VLDB 2020 SIGMOD 2017 Reproducibility Award Grants: NSF IIS-2348919 (2024-2027): Fairness-aware Data Structures NSF IIS-2107290 (2021-2024): Collaborative Fairness Research The InDeX Lab develops systems like Needle (image retrieval) and RSR (matrix multiplication). His work integrates fairness , reliability , and computational efficiency across data structures , LLMs , and responsible AI implementations.

• Behavioural Economics
• Evolutionary Game Theory
• Social Networks
• +5 more

Friederike Mengel is a Professor of Economics at the University of Essex and holds a Visiting Professorship at Erasmus University Rotterdam. She is also a Fellow of the Academy of Social Sciences (UK). Her research focuses on Behavioural Economics , integrating Game Theory , Evolutionary Dynamics , and Social Network Analysis . Key research themes include social influence in networks , opinion dynamics , emergence of social norms , and links between social identity, bounded rationality, and discrimination . Recent work explores Covid-19 impacts on productivity and innovation in hybrid work environments . Her scientific awards include the Best Paper Award from Quantitative Economics (2018) and recognition as an Academy of Social Sciences Fellow. Her publications span topics like cooperation in viscous populations , strategic behavior in repeated games , and gender bias in opinion aggregation , with media coverage in outlets like The Economist and Financial Times .

Qi LongView profile

Professor

• Genomics
• Computational Biology
• Biostatistics
• +10 more

Qi Long is a Professor at the University of Pennsylvania, holding joint appointments in the Department of Biostatistics, Epidemiology and Informatics (Perelman School of Medicine), Department of Computer and Information Science (School of Engineering and Applied Science), and Department of Statistics and Data Science (The Wharton School). He serves as Founding Director of the Center for Cancer Data Science, Associate Director of the Penn Institute for Biomedical Informatics, and Associate Director for Quantitative Data Science at the Abramson Cancer Center. His research bridges statistical and machine learning (ML/AI) method development with biomedical applications, focusing on precision medicine and population health. Education : Ph.D. (2005) and M.S. (2003) in Biostatistics from University of Michigan; B.S. (1998) in Computer Science from University of Science and Technology of China. Research interests include: Robust statistical and ML/AI methods for big health data (-omics, EHRs, imaging, mHealth) Multimodal data integration and subgroup heterogeneity analysis Missing data, causal inference, Bayesian methods, and clinical trials Data privacy, algorithmic fairness, and responsible AI in healthcare Foundation models and agentic AI for biomedicine His publications focus on privacy-preserving AI, fairness-aware ML, and integrative models for multi-omics and EHRs. Recent work explores LLMs and watermark detection in hybrid human-AI settings. Scientific Awards : Elected fellow: AAAS, ASA, IMS, ISI, AMIA He leads large NIH- and ARPA-H-funded initiatives, directing statistical coordinating centers for national clinical trials. His lab trains numerous PhD/Master’s students and postdocs, many of whom hold prestigious academic or industry positions.

Anjalie FieldView profile

Assistant Professor

• Natural language processing
• Computational social science
• AI ethics
• +3 more

Anjalie Field is an Assistant Professor in the Computer Science Department at the Whiting School of Engineering, Johns Hopkins University. She is also affiliated with the Data Science and AI Institute and the Center for Language and Speech Processing (CLSP). Dr. Field completed her PhD at the Language Technologies Institute at Carnegie Mellon University under Yulia Tsvetkov, where she was a member of TsvetShop. Prior to joining Johns Hopkins, she was a postdoctoral researcher in the Stanford NLP Group and at the Stanford Data Science Institute, working with Dan Jurafsky and Jennifer Eberhardt. She also spent time as a visiting student at the University of Washington from 2021 to 2022. Her research focuses on the ethics and social science aspects of natural language processing, developing computational models to address societal issues like discrimination and propaganda while critically assessing and improving privacy, transparency, and fairness in AI pipelines. Her work spans social media analysis, bias detection in multilingual content, police accountability systems, and ethical considerations in large language model applications. Dr. Field's publications demonstrate a consistent focus on identifying and addressing biases in language technologies, with particular attention to racial, gender, and cultural dimensions. Her recent work has expanded into domain-specific applications of NLP in astronomy, healthcare, and social justice contexts, showing the breadth of impact that ethical AI considerations can have across disciplines. Among her notable recognitions are: AI2050 Fellow by Schmidt Sciences 2022 Wikimedia Foundation Research Award of the Year Best Paper nomination at Socinfo (2020) Dr. Field teaches AI Ethics and Social Impact (Fall 2023; Fall 2024) and NLP for Computational Social Science (Spring 2024; Spring 2025). She plans to take PhD students for the 2024-2025 admissions cycle. Her work bridges technical NLP research with important social considerations, making significant contributions to both the technical community and broader societal discourse around AI ethics. She is an active member of the Center for Language and Speech Processing, where she collaborates with researchers working at the intersection of language technologies and real-world applications. Her research focuses on developing methods that not only advance NLP capabilities but also ensure these technologies serve diverse communities equitably.

Ohad FriedView profile

Associate Professor

• Computer Graphics
• Computer Vision
• Generative AI (GenAI)
• +5 more

Ohad Fried is an Associate Professor of Computer Science at Reichman University. He was previously a postdoctoral research scholar at Stanford University under Prof. Maneesh Agrawala and completed his PhD with Prof. Adam Finkelstein as part of the Princeton Graphics group. He holds an M.Sc. in Computer Science and a B.Sc. in Computational Biology from The Hebrew University. His research lies at the intersection of computer graphics, computer vision, and Generative AI , focusing on tools, algorithms, and paradigms for photo and video editing and synthesis . His work has been widely recognized in top conferences including CVPR, SIGGRAPH, and ECCV, with recent contributions to tiled diffusion models, expressive 4D facial motion generation, and synthetic image detection. Ohad has received numerous awards, including the Israel Science Foundation personal research grant (2021) , the Outstanding faculty researcher at Reichman University (2022) , and the Siebel Scholar award (2017) . He has advised multiple students in research projects, and his work is covered by media outlets like Wired , The Washington Post , and CNN . Teaching roles include courses at Reichman University such as "GenAI for Games & Entertainment" and "Synthetic Media Detection", and at Stanford University "Computational Video Manipulation". Key Research Themes: Neural Rendering Diffusion Models 3D Facial Animation Image/Video Editing Media Forensics Scientific Awards: ISF Personal Grant (2021) Siebel Scholar (2017) Google PhD Fellowship (2014-2016) Gordon Y.S. Wu Fellowship (2012-2013) Excellence Scholarships

• Health IT
• Healthcare Analytics
• Healthcare Operations Management
• +5 more

Gaurav Jetley is an Assistant Professor in the Department of Computer Information Systems at Colorado State University. Education: PhD in Business Administration (Information Systems) from Muma College of Business, University of South Florida Research Interests: Focuses on healthcare technology, including health IT, healthcare analytics, and healthcare operations management. Employs data-driven methodologies, machine learning, and econometric techniques to solve complex problems in health information systems. Scientific Contributions: Recent publications highlight his work in EHR quality, racial bias in ICU pain measurement, and health IT workflow optimization, demonstrating interdisciplinary applications of business analytics in healthcare. Teaching: Instructs graduate and undergraduate courses in data mining and analytics.