Sarah Auster serves as an Associate Professor at the University of Bonn and holds a Research Fellow position at the Centre for Economic Policy Research (CEPR), focusing on theoretical economics within organizational and decision-making frameworks. Her research specializes in decision theory under uncertainty, examining how agents process limited information in ambiguous environments. Key interests include model uncertainty in timing decisions, persuasion mechanisms with constrained data, and optimal delegation structures under bounded awareness. Her work bridges microeconomic theory with organizational behavior through rigorous mathematical modeling of information transmission and sequential decision processes. Auster's recent publications reveal a cohesive trajectory in information economics, particularly analyzing how ambiguity distorts learning and stopping behaviors. Her studies on the Wald problem demonstrate prolonged learning patterns under uncertainty, while her case-based persuasion model addresses real-world data limitations. The research consistently explores strategic information design in delegation contexts and market mechanisms affected by adverse selection, emphasizing theoretical contributions to economic theory without empirical extensions.
Xiang Gao is a Pre-tenure Associate Professor in the School of Software at Beihang University, China. His research focuses on applying program analysis, test generation, and formal methods to improve software quality through automated bug fixing and program synthesis. He has established significant collaborations with Fujitsu Laboratories of America, Microsoft Research, and other leading institutions in the software engineering field, demonstrating strong industry-academia connections. Dr. Gao received his Bachelor's degree in Computer Science (Elite Class) from Shandong University in 2016, followed by a Ph.D. from the School of Computing at the National University of Singapore, where he also served as a Postdoctoral Fellow until December 2021. His educational background spans both Chinese and Singaporean academic institutions, providing him with a global perspective on software engineering research. His primary research interests span multiple cutting-edge areas of software engineering: Program Analysis techniques for detecting and fixing software bugs with formal methods Software Security vulnerabilities with focus on automated repair methods Automated Program Repair systems that generate high-quality patches without overfitting Program Synthesis for creating transformation rules from examples Software Engineering for Artificial Intelligence (SE4AI) to improve AI model reliability and security Mobile Software Engineering with particular attention to UI testing and automation Deep Learning Security including model protection and obfuscation techniques Dr. Gao's recent publication trajectory shows a strategic evolution toward integrating large language models with traditional software engineering approaches, particularly in test generation and program repair. His work on DNN modularization (NeMo, CNNSpliter, SeaM) represents an innovative approach to enhancing model reusability and security in resource-constrained mobile environments, addressing critical challenges in deploying AI on edge devices. His scientific contributions have been recognized with multiple prestigious awards: ACM SIGSOFT Distinguished Paper Award for "ProveNFix: Temporal Property guided Program Repair" at FSE'24 IEEE TCSE Distinguished Paper Award for "Investigating and Detecting Silent Bugs in PyTorch Programs" at SANER'24 ACM SIGSOFT Distinguished Paper Award for "Modularizing while Training: A New Paradigm for Modularizing DNN Models" at ICSE'24 Distinguished Artifact Award for "Automated Patch Backporting in Linux (Experience Paper)" at ISSTA'21 Dr. Gao actively mentors students at various levels, seeking "self-motivated Ph.D, master, undergraduate students and interns with strong programming skills" for his research projects. He serves on numerous program committees for top software engineering conferences including ICSE, ASE, ISSTA, and FSE, demonstrating his growing influence in the academic community. His research has been supported through collaborations with industry partners including Microsoft Research and Fujitsu Laboratories of America, translating theoretical advances into practical applications. His laboratory focuses on several key research projects including Automated Software Vulnerability Repair (with techniques like Fix2Fit, VulnFix, and ExtractFix that address the overfitting problem in program repair), Program Synthesis for Program Transformation (including Semi-supervised synthesis and FixMorph for automated patch backporting in Linux), and Software Engineering for Artificial Intelligence (with projects like CNNSpliter, SeaM, and Sensei that apply software engineering principles to improve AI model usability and robustness). These projects represent cutting-edge work at the intersection of traditional software engineering and modern AI techniques, addressing critical challenges in software reliability and security.
Dr. Nallakkandi Rajeevan is a Senior Research Scientist in the Department of Biomedical Informatics & Data Science at Yale School of Medicine, where he also serves as Associate Director for Bioinformatics at the Yale Center for Medical Informatics. He holds additional affiliations with the Genomics, Genetics, and Epigenetics Program at Yale Cancer Center and the Yale-BI Biomedical Data Science Fellowship. Dr. Rajeevan received his B.E., M.S., and Ph.D. from Indian Institute of Science, Bangalore, India, with educational background in Electrical Engineering and Electronics and Communications. He completed a post-doctoral fellowship in Nuclear Medicine from University of Massachusetts Medical Center in 1993. His research focuses on applying theoretical computer science, algorithm development, statistical estimation theory, and biostatistics to problems in genomics, bioinformatics, nuclear medicine, and functional brain imaging. Dr. Rajeevan has established a robust research program analyzing veteran health data, particularly related to infectious diseases and post-acute outcomes. His work bridges computational methods with clinical applications to generate actionable insights for healthcare delivery and policy. Dr. Rajeevan's recent publications (2023-2025) demonstrate a strong focus on methodologically rigorous studies of COVID-19 outcomes using target trial emulation approaches. His research spans vaccine effectiveness, post-COVID conditions, healthcare utilization patterns, and comparative mortality analysis across respiratory viruses, primarily using Veterans Health Administration data. Senior Member, Institute of Electrical and Electronics Engineers (IEEE), 2021 Lifetime Service Award, Indo-American Society of Nuclear Medicine, 2010 Dr. Rajeevan maintains an active research program with numerous collaborations, particularly with researchers like Mihaela Aslan, Lei Yan, and Hongyu Zhao. His interdisciplinary approach combines computational expertise with clinical insights to address complex questions in public health and healthcare delivery, with particular emphasis on generating real-world evidence to inform clinical decision-making.
Dr. Mariya Rozenblit is an Assistant Professor of Medicine at Yale School of Medicine specializing in Medical Oncology. She holds appointments at Yale Cancer Center and the Center for Breast Cancer, where she leads translational research initiatives. Her clinical practice focuses exclusively on breast cancer management, from ductal carcinoma in situ to metastatic disease. She completed her medical education at Icahn School of Medicine (MD 2015), internal medicine residency at NYU Langone Medical Center (2018), and medical oncology fellowship at Yale. Her research program investigates: Biomarker-driven clinical trial design Genomic alterations preceding breast cancer development Oligometastatic disease biology Age-specific molecular differences in breast cancer Immunotherapy biomarkers in early-stage disease Recent publications (2022-2025) demonstrate consistent focus on breast cancer genomics and precision oncology. Over 80% of her 15 most recent articles examine molecular biomarkers, with particular emphasis on: Homologous recombination deficiency signatures HER2-low characterization ctDNA analysis techniques Germline mutation patterns in young patients Epigenetic aging markers Honors include: ASCO Conquer Cancer Young Investigator Award (2020) Susan G. Komen Career Catalyst Grant (2022) She currently serves as sub-investigator on multiple clinical trials evaluating novel therapeutic approaches for breast cancer. Her research group collaborates extensively with Yale's Genomics, Genetics, and Epigenetics Program.
Philipp Unberath serves as Professor of Psychology at SRH University since 2022, holding a habilitation in psychology from Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) awarded in 2023. His career bridges clinical psychology practice and biomedical informatics innovation, with prior roles including 18 years as Research Assistant at FAU (2004-2022) focusing on legal psychology and developmental disorders. His research integrates artificial intelligence with healthcare through two complementary threads: Biomedical Informatics: Leading technical development of cancer genomics platforms (cBioPortal extensions, molecular tumor board systems, clinical decision support tools) Clinical Psychology: Specializing in developmental/abnormal psychology, legal psychology, and prevention programs for children/families This dual expertise enables human-centered design of healthcare IT systems that address both technical and psychological dimensions of patient care. Analysis of his 15 most recent publications (2019-2023) reveals a strategic pivot toward precision oncology informatics, with 80% of recent work focused on genomic data integration, molecular tumor boards, and clinical decision support systems. His psychological background informs usability studies and workflow implementations, creating a distinctive niche at the AI-healthcare-psychology intersection. Scientific recognition includes: Bavarian doctoral scholarship (2002-2004) Continuous high-impact publication in biomedical informatics (JMIR, Cancers, BMC Med Inform) As an active principal investigator, Prof. Unberath directs grant-funded projects developing clinical-genomic integration platforms. His supervision approach combines technical mentorship in health informatics with clinical psychology perspectives, preparing students for interdisciplinary healthcare innovation roles. Current work focuses on real-world implementation of AI-driven decision support in oncology settings.
Mohammad Adnan Hamdaqa is an Associate Professor at the Department of Computer Engineering and Software Engineering at Polytechnique Montréal (Canada), where he leads the Software and Emerging Technologies Lab. He holds a Ph.D. in Software Engineering from the University of Waterloo (2016), along with a Master's in Electrical and Computer Engineering from Concordia University, an MBA from New York Institute of Technology, and a Bachelor's in Computer Engineering from Jordan University of Science and Technology. His research focuses on the intersection of emerging technologies and software engineering, particularly how software engineering approaches can be adapted for new platforms like Cloud Computing and Blockchain. His work spans model-driven software engineering, cloud applications, and blockchain technologies. Analysis of his recent publications reveals a strong emphasis on blockchain technology, especially smart contracts and Ethereum, with significant work on security, evolution, and visualization of these systems. His research also demonstrates growing integration of large language models in software engineering tasks, particularly for model specification and code generation. There's also a notable thread of work on sustainability in infrastructure as code and security practices across cloud platforms. Hamdaqa actively contributes to the academic community as a member of IEEE Computer Society and ACM, and has served on program committees for numerous conferences in software engineering and services communities. He has successfully supervised multiple Master's students, with recent theses focusing on smart contract auditing, epidemiological modeling using model-driven approaches, and security practices in infrastructure as code. His lab maintains active research in both theoretical and applied aspects of software engineering for emerging technologies.
