RISK-SENSITIVE STATISTICAL LEARNING: METHODS, ALGORITHMS, AND THEORIES -THE RESEARCH IN THIS PROJECT WILL A…

Assistance award details

Assistance award identifier

ASST_NON_2346292_049

FAIN

2346292

Recipient

DUKE UNIVERSITY

Awarding agency

National Science Foundation

Funding agency

National Science Foundation

Assistance listing (CFDA)

47.049

Total obligated

$337,985

Period

Jun 15, 2024 – May 31, 2027

Fiscal year

2024

Place of performance state

NC

Source file

FY2024_All_Assistance_Full_20261006.zip

Description

RISK-SENSITIVE STATISTICAL LEARNING: METHODS, ALGORITHMS, AND THEORIES -THE RESEARCH IN THIS PROJECT WILL ADDRESS A FUNDAMENTAL CHALLENGE IN A VARIETY OF CRITICAL FIELDS SUCH AS MEDICINE, FINANCE, AND ROBOTICS: MAKING DECISIONS THAT NOT ONLY AIM FOR THE BEST AVERAGE OUTCOMES BUT ALSO MINIMIZE RISKS TO INDIVIDUALS. FOR INSTANCE, WHEN SELECTING A MEDICAL TREATMENT, IT IS NOT ENOUGH TO KNOW THAT A TREATMENT WORKS WELL ON AVERAGE; WE NEED TO ENSURE THAT IT BENEFITS A LARGE PROPORTION OF PATIENTS, SAY 95%, WITHOUT CAUSING HARM. CURRENT STATISTICAL LEARNING METHODS, HOWEVER, OFTEN OVERLOOK SUCH NUANCED CONSIDERATIONS OF RISK, LEADING TO A SIGNIFICANT DISCONNECT BETWEEN THEORETICAL ADVANCEMENTS AND PRACTICAL NEEDS. THIS PROPOSAL SEEKS TO CLOSE THIS GAP BY DEVELOPING A NEW FRAMEWORK FOR RISK-SENSITIVE DECISION-MAKING. BY INCORPORATING RISK ASSESSMENTS INTO THE DECISION-MAKING PROCESS, OUR RESEARCH AIMS TO ADVANCE SCIENTIFIC KNOWLEDGE AND CONTRIBUTE TO THE GENERAL INTEREST BY IMPROVING HEALTHCARE OUTCOMES, FINANCIAL STABILITY, AND THE SAFETY AND EFFECTIVENESS OF ROBOTIC SYSTEMS. THE PROJECT WILL ALSO SUPPORT EDUCATION AND PROMOTE DIVERSITY BY PROVIDING TRAINING OPPORTUNITIES FOR STUDENTS AT VARIOUS LEVELS AND DEVELOPING USER-FRIENDLY, OPEN-SOURCE SOFTWARE TO MAKE THESE ADVANCED METHODS ACCESSIBLE TO PRACTITIONERS. THE RESEARCH WILL FOCUS ON THE DEVELOPMENT OF INNOVATIVE METHODS FOR RISK-SENSITIVE STATISTICAL LEARNING, AIMING TO IMPROVE DECISION-MAKING PROCESSES IN AREAS WHERE OUTCOMES' VARIABILITY CAN SIGNIFICANTLY IMPACT OVERALL SUCCESS. THE PROJECT IS STRUCTURED AROUND FOUR SPECIFIC AIMS. THE FIRST AIM IS TO DEVELOP QUANTILE-CONSTRAINED STATISTICAL LEARNING. A PARTICULAR EXAMPLE IS TO OPTIMIZE AVERAGE OUTCOMES UNDER CONSTRAINTS THAT ENSURE A MINIMUM BENEFIT LEVEL ACROSS A DEFINED PROPORTION OF A POPULATION. THIS INVOLVES FORMULATING THE PROBLEM AS A CONSTRAINED OPTIMIZATION ISSUE WITH STOCHASTIC BOUNDARIES, PROPOSING NOVEL ALGORITHMS, AND DERIVING ASYMPTOTIC RESULTS FOR ES