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Yael Grushka-CockayneView profile

Professor

• Data Science
• Forecasting
• Project Management
• +4 more

Professor Yael Grushka-Cockayne is a faculty member at the University of Virginia's Darden School of Business. Her academic career spans research and teaching in Data Science, Forecasting, Project Management , and Behavioral Decision-Making . She holds degrees from Ben-Gurion University (B.Sc.), London School of Economics (M.Sc.), and London Business School (Ph.D., M.Res.). Current affiliations: Darden School of Business (University of Virginia) Academic rank: Professor Professional memberships: INFORMS, Decision Analysis Society, Operational Research Society, Project Management Institute (PMI) Her research focuses on data-driven decision-making , project scheduling , and forecasting methodologies . Recent publications emphasize AI applications in business coding and technical decision frameworks . She has authored influential studies on ensemble forecasting , quantile evaluation , and behavioral biases in project planning . Key trends in her recent work (2023-2024) include: AI integration in business coding and software development Technical decision economics and resource allocation Project management methodology innovation Behavioral aspects of software development and digital transformation Scientific recognition includes: Darden Morton Leadership Faculty Award (2011) Mead-Colley Award (2012) Darden Outstanding Faculty Award (2013, 2022) University of Virginia All University Teaching Award (2015) Multiple Transformational Faculty Awards (2024, 2025) She leads executive education programs like AI Essentials for Government Leaders and has developed digital learning platforms such as the Coursera MOOC Fundamentals of Project Planning and Management (350,000+ learners). As associate editor at Management Science and Operations Research , she shapes academic discourse in decision analysis and operations management.

• financial econometrics
• functional analysis
• probability theory
• +4 more

Borzykh Dmitry Alexandrovich is an Associate Professor at the National Research University Higher School of Economics , affiliated with the Department of Applied Economics under the Faculty of Economic Sciences . He also serves as a Research Fellow at the International Laboratory of Stochastic Analysis and its Applications . With 18 years of scientific and teaching experience since joining HSE in 2006, he teaches advanced courses in probability theory, mathematical statistics, and econometrics at both undergraduate and graduate levels. Education: Candidate of Physical and Mathematical Sciences (2022, HSE), Master's in Economics (2006, HSE), Bachelor's in Economics (2004, HSE) Key research areas: financial econometrics , stochastic analysis , structural breaks in time series , and stochastic volatility models His recent publications focus on stochastic processes , structural break detection , and quantile function applications across financial and economic modeling. He has received multiple institutional awards, including Best Teacher (2014, 2016–2025) and formal gratitudes from HSE departments (2019, 2022). He provides consultations via email and holds office hours at Pokrovsky Boulevard campus (room S517).

Feng LiuView profile

Assistant Professor

• Machine Learning
• Hypothesis Testing
• Trustworthy Machine Learning
• +3 more

Feng Liu is an Assistant Professor at the Decision Systems and e-Service Intelligence (DeSI) Lab within the Australian Artificial Intelligence Institute (AAII) at the University of Technology Sydney (UTS). He also serves as a Visiting Scientist at RIKEN-AIP, Japan. His academic journey includes a PhD in Computer Science from UTS (2020), an MSc in Probability and Statistics from Lanzhou University (2015), and a BSc in Mathematics from the same institution (2013). His educational background includes: Ph.D. (2020), Computer Science, University of Technology Sydney, Australia M.Sc. (2015), Probability and Statistics, Lanzhou University, China B.Sc. (2013), Mathematics, Lanzhou University, China Feng Liu's research centers on developing trustworthy intelligent systems through hypothesis testing and reliable knowledge transfer across domains. His work spans two-sample testing for distribution comparison, transfer learning for knowledge adaptation across domains, and defending against adversarial attacks to improve model robustness. His approach combines theoretical foundations with practical applications, particularly in domain adaptation with interval-valued data and secure multi-source learning. His recent publications demonstrate a strong focus on trustworthy machine learning, with significant contributions to interval-valued data processing, novel class discovery under unreliable sampling conditions, and privacy-preserving domain adaptation. His work bridges theoretical machine learning with practical applications in computer vision, bioinformatics, and recommender systems, showing a consistent pattern of addressing fundamental challenges in trustworthy AI. Among his notable recognitions are: Outstanding Reviewer Award of ICLR (2021) AAII Best Student Paper Award (2020) Best Student Paper Award from IEEE International Conference on Fuzzy Systems (2019) UTS-FEIT HDR Research Excellence Award (2019) Publons Peer Review Awards - Top 1% reviewers in Computer Science (2019, 2018) Dr. Liu has actively contributed to the academic community through supervision and service. He has helped supervise four students who collectively produced eight academic papers, three of which were published in CORE Tier A* venues. His service includes program committee roles for major conferences including NeurIPS, ICML, ICLR, and AAAI, as well as reviewing for prestigious journals like IEEE-TPAMI and IEEE-TNNLS. His research has been supported by various grants, including the Australian Laureate postdoctoral fellowship. As part of the AAII at UTS, Dr. Liu contributes to a vibrant research environment focused on advancing artificial intelligence through interdisciplinary collaboration. His work in the Decision Systems and e-Service Intelligence Lab addresses real-world challenges in trustworthy machine learning, with applications spanning healthcare, robotics, and secure information systems.

Loïc AdamView profile

Associate Professor

• Incremental Preference Elicitation
• Multicriteria Decision Making
• Decision Theory under Risk and Uncertainty
• +8 more

Loïc Adam serves as an Associate Professor in Data Engineering at ISAE-ENSMA since 2024, concurrently holding a lecturer position in Digital Information. Previously, he was an ATER at Université de Technologie de Compiègne (2023-2024) where he taught operational research and data analysis courses. He completed his PhD in Computer Science at UTC in 2023 and currently serves on ENSMA's improvement council and scientific expertise commission (2025-2027). His research spans decision theory under risk and uncertainty, with core expertise in incremental preference elicitation, multicriteria decision-making, and uncertainty modeling through possibility theory and belief functions. He actively investigates information fusion, machine learning applications, and game-theoretic approaches to preference learning, focusing on resolving inconsistent preferences and developing robust decision support frameworks under severe uncertainty. Adam's publication record (2020-2024) reveals consistent contributions at the intersection of artificial intelligence and decision science, particularly in preference modeling and uncertainty quantification. His work demonstrates methodological innovation in applying possibility theory to inconsistency resolution and developing imprecise probabilistic models for label ranking, with publications spanning top venues in AI, fuzzy systems, and decision theory. Scientific Awards: No major awards or fellowships were documented in the source material. He currently supervises M2 intern Aicha AIT HAMMOUCH on auto-detection of erroneous models through personalized data elicitation in uncertain environments. As a member of LIAS Laboratory's Data Engineering team, he collaborates across ENSIP (University of Poitiers) and ISAE-ENSMA sites, contributing to interdisciplinary projects that bridge theoretical decision science with real-world engineering applications in control systems and real-time data processing.