Joakim Sundnes is a Chief Research Scientist and Research Professor at the Department of Scientific Computing, Simula Research Laboratory. He specializes in computational physiology, cardiac biomechanics, and mathematical modeling of cardiovascular systems. Key Research Areas: Cardiac electromechanics, computational fluid dynamics in cardiology, uncertainty quantification in cardiac models, and mechano-electric feedback mechanisms Recent Trends: Focus on patient-specific modeling, left atrial flow dynamics, right ventricular mechanics in pulmonary hypertension, and personalized treatment simulations Scientific Contributions: Active participant in international conferences and editorial work. Co-author of multiple benchmark studies and educational texts on physiological modeling.
Jasper Goseling is an Associate Professor at the Digital Society Institute and affiliated with the Mathematics of Operations Research department. His research spans differential privacy, network coding, optimization, and wireless systems, often bridging theoretical and applied domains. Key research areas: Differential Privacy, Network Coding, Optimization, Wireless Sensor Networks, Machine Learning His recent work focuses on robust optimization techniques for local differential privacy, addressing trade-offs between data utility and privacy preservation. Earlier contributions include studies on energy-efficient data collection in sensor networks, caching strategies in wireless environments, and entropy-based analysis of hydrothermal systems. Article trends reveal a strong emphasis on privacy-preserving algorithms (2022-2024) and historical expertise in network coding, queueing theory, and thermodynamic entropy. His research integrates mathematical rigor with practical applications in wireless communication and data management. Activities include organizing the 45th Symposium on Information Theory and Signal Processing (2025) and leadership roles in the IEEE Benelux Chapter on Information Theory (Chair, 2017; Member, 2012-2017). He also contributed to the 2015 European School of Information Theory.
Chi Wang, Ph.D., is Professor of Internal Medicine and Statistics at the University of Kentucky, Associate Director of the Biostatistics & Bioinformatics Shared Resource, and a lead computational scientist within the Markey Cancer Center. With an h-index of 39 and >5,300 citations, he develops cutting-edge survival-analysis and high-dimensional-omics methods that are translated into prostate, breast, lung and neuroblastoma trials. Education: Ph.D. Biostatistics, Johns Hopkins University, 2009 M.S. Statistics, Peking University, 2003 B.S. Mathematics, Peking University, 2001 Research Focus: Wang’s group integrates Bayesian statistics, machine-learning and single-cell sequencing to model tumor evolution, drug resistance and biomarker-driven adaptive trials. Major themes include enzalutamide resistance in prostate cancer, PLK1-mediated immune evasion in lung adenocarcinoma, obesity-adipose drivers of breast cancer, and epigenetic reprogramming of tumor-infiltrating lymphocytes. Recent Article Trends (2023-2025): Fifteen showcased papers reveal a dual trajectory—(1) mechanistic dissection of kinases (PLK1, DDR1, ABL1/2) and metabolic enzymes (FASN, NNMT, HSP47) that orchestrate resistance and metastasis, and (2) probabilistic algorithms for temporal mutation ordering, biomarker evaluation and single-cell deconvolution. Overarching keywords are precision oncology, drug resistance, tumor micro-environment, and computational systems biology. Funding & Awards: He is PI/co-I on 152 grants (32 active) from NCI, ACS, V Foundation, etc., totaling >$30 M direct costs. Highlights include NCI U01 for predictive biomarkers, American Cancer Society award on collagen lysyl hydroxylase metastasis drivers, and NCI R01s targeting NNMT in triple-negative breast cancer and TIL epigenetic reprogramming in NSCLC. Student & Team Mentorship: Wang trains a large multi-disciplinary cohort of Ph.D. students, post-docs and clinical fellows who routinely lead first-author papers and are funded on his grants. His lab sits within the Markey Cancer Center’s Molecular & Cellular Oncology program and the Center for Computational Sciences, fostering daily bench-to-bedside collaboration.
Ajay Kalra serves as an Associate Professor of Water Resources Engineering in the Civil Engineering Department at Southern Illinois University Carbondale's College of Engineering. His office is located in Engineering Building, Room 114, where he teaches undergraduate and graduate courses including Fluid Mechanics, Open Channel Hydraulics, Water Resources Engineering, and Advanced Hydraulic Design. Dr. Kalra's research spans hydro-climatology, urban sustainability, artificial intelligence applications in water resources, drought frequency analysis, and probabilistic forecasting. His interdisciplinary work connects climate science with practical water management solutions, focusing on how large-scale climate patterns influence regional hydrology. He has developed innovative approaches using machine learning to improve streamflow forecasting and drought prediction, particularly in western U.S. river basins. His scholarly output shows consistent productivity with research trends indicating increasing focus on machine learning applications for hydrological prediction, climate change impacts on water resources, and urban flood management. Recent publications demonstrate strong integration of remote sensing data with traditional hydrological models to address water challenges in both gauged and ungauged basins. Outstanding dissertation award 2011 (UNLV) Outstanding dissertation award 2011 (UNLV-College of Engineering) Best poster award (2nd Place, UNLV-College of Engineering) GPSA merit award 2010-2011 Member Tau Beta Pi (Engineering Honor Society) Dr. Kalra actively mentors graduate students and collaborates with researchers across institutions. His professional service includes membership in the American Geophysical Union and American Society of Civil Engineers. His current research program continues to explore the connections between oceanic-atmospheric oscillations and regional hydrology while expanding into urban water sustainability challenges under changing climate conditions.
Emre Ugur is an Associate Professor in the Department of Computer Engineering at Bogazici University, where he serves as the head of the Cognition, Learning and Robotics (CoLoRs) laboratory. His research focuses on bridging the gap between continuous sensorimotor experiences and discrete symbolic representations in robotics. Funded by major international sources including the European Commission's Horizon 2020 program and TUBITAK, his work has significant implications for cognitive robotics and autonomous systems. Education: PhD in Computer Engineering from Middle East Technical University (METU, Turkey) Ugur's research interests center on cognitive and developmental approaches to robotics, with particular emphasis on neuro-symbolic integration, affordance learning, and symbol emergence. His work explores how robots can autonomously develop high-level cognitive capabilities through continuous interaction with their environment, similar to human cognitive development. His approach combines machine learning, cognitive science, and robotics to create systems that can learn, predict, and reason about their actions. His recent publications reveal a strong trajectory toward neuro-symbolic robotics, where he develops methods for extracting discrete symbolic representations from continuous sensorimotor experiences. This work enables robots to perform complex planning and reasoning tasks while maintaining connection to physical reality. There is also significant focus on social robotics, particularly in human-robot interaction, social navigation, and embodied cognition. Scientific Awards: The Young Scientist Award by the Science Academy (BAGEP) The Excellence in Teaching Award by the Faculty of Engineering (2023) As Principal Investigator of major projects including INVERSE (EU Horizon 2025), DEEPPLAN (TUBITAK), and previously DEEPSYM and IMAGINE, Ugur has established a robust research program that bridges theoretical advances with practical applications. He has supervised numerous PhD and Master's students who have made significant contributions to the field. His leadership extends to organizing major workshops at top robotics conferences including IROS, RSS, and ICRA. At the Cognition, Learning and Robotics (CoLoRs) lab, Ugur leads research on cognitive robotics, developmental robotics, and neuro-symbolic AI. The lab explores fundamental questions about how robots can develop understanding of their actions, learn from interaction, and form abstract representations necessary for high-level cognition. Current projects focus on symbolic reasoning, prediction, and planning in robotic systems.
Professor Otso Ovaskainen holds a permanent position at the University of Helsinki , where he serves as Research Director in the Organismal and Evolutionary Biology Research Program. He is also a Visiting Professor at a Norwegian Centre of Excellence since 2014. Supervisor in the Doctoral Programme in Wildlife Biology Established the Research Center for Ecological Change (REC) with 60+ members ERC Starting Grant recipient (2008-2013) Research Interests: Ovaskainen bridges mathematical theory with empirical ecology, focusing on metapopulation dynamics, movement ecology, and statistical community ecology. His work integrates: Ecological theory with data Individual-based modeling Fungal community dynamics Probabilistic taxonomic placement Environmental DNA analysis Multi-species movement modeling Scientific Leadership: He has led major projects including the Finnish Centre of Excellence in Metapopulation Research (2015-2017) and the LIFEPLAN biodiversity inventory project (2020-2026). His HMSC modeling framework remains a benchmark in joint species distribution modeling.
Marc Jochen Uetz is a Full Professor at the Mathematics of Operations Research department and affiliated with the Digital Society Institute. His research spans operations research and computer science, focusing on scheduling, game theory, and optimization problems. PhD in Mathematics from Technische Universität Berlin Research Interests: Active in algorithmic game theory and stochastic scheduling, he investigates equilibrium models, price of anarchy, and efficient resource allocation in transportation and network systems. His work contributes to UN Sustainable Development Goals related to education and infrastructure. Publication Trends: Recent work combines game theory with two-stage facility location, network routing, and stochastic scheduling of Bernoulli-type jobs. Key keywords include Nash equilibrium, approximation algorithms, and dynamic programming. Scientific Recognition: Excellent Reviewer Award (2017) Teaching Award (2018) Academic Activities: Currently chairs Platform Wiskunde Nederland, contributes to editorial work, and delivers invited talks at international conferences like IJCAI 2024.
Eric Atkinson is an Assistant Professor in the School of Computing at Binghamton University, specializing in programming languages for uncertainty and their intersections with artificial intelligence. He holds a PhD from MIT (2024), an MS from MIT (2018), and a BS from UC Berkeley (2015). Prior to Binghamton, he was a visiting researcher at INSAIT in Sofia, Bulgaria, and conducted research internships at Facebook and Mozilla. His research focuses on programming languages, program runtimes, formal methods, and AI integration. Key interests include probabilistic programming, static analysis, and runtime systems for uncertain domains. He teaches programming languages courses and advises PhD and M.Sc. students. His publications span probabilistic programming systems, compiler optimizations, and formal verification. He actively participates in academic service roles, including program committees for PLDI, LAFI, and OOPSLA, and mentors underrepresented groups in graduate school through initiatives like the MIT EECS GAAP program.
Karen Eilbeck is Professor of Biomedical Informatics and Adjunct Associate Professor of Human Genetics at the University of Utah , where she directs a bioinformatics research lab focused on precision medicine and genomic data management. Education B.S., University of Salford, United Kingdom Ph.D., University of Manchester, United Kingdom Research Interests The Eilbeck lab leverages computer science and ontology engineering to address contemporary questions in genomics and molecular biology. Central themes include: Development of ontologies such as the Sequence Ontology (SO) and Non-Coding RNA Ontology (NCRO) to standardize and structure biological data. Design of ontology-enabled software tools that enhance data sharing, variant interpretation, and precision medicine workflows. Metagenomic pathogen detection, clinical genome interpretation, and integration of multi-omics datasets to advance personalized healthcare. Publication Trends Recent publications (2023-2025) emphasize AI-driven clinical decision support, standardized genomic terminology, and large-scale infectious-disease risk modeling. Earlier works (2005-2018) established foundational ontologies and data formats (GVF, VCF) now widely adopted by the genomics community. Contact & Resources Email: keilbeck@genetics.utah.edu Lab website: available via University of Utah Department of Biomedical Informatics Full publication list: PubMed search | Google Scholar
Sunil Aryal is an Associate Professor of Data Science at the School of Information Technology, Faculty of Science Engineering and Built Environment, Deakin University, Australia. He received his PhD and Master by Research degrees from Monash University Australia and has published over 70 papers in top-tier international venues in Artificial Intelligence, Machine Learning and Data Mining. Dr. Aryal's educational background includes: Graduate Certificate of Higher Education Learning and Teaching, Deakin University (2020) PhD in Computer Science, Monash University (2017) Master of Information Technology (Research), Monash University (2012) Master of Information Technology (Coursework), University of Southern Queensland (2008) Bachelor of Information Technology, Purbanchal University, Nepal (2005) His primary research interests focus on making Machine Learning and Data Mining algorithms robust and flexible to handle heterogeneous, noisy and uncertain data in real-world problems. His work spans across several specific areas including anomaly detection, clustering, kernel/similarity-based learning, ensemble methods, learning from limited data, reinforcement learning, natural language processing, and computer vision. Dr. Aryal is particularly interested in applying these techniques to solve challenges in Defence, National Intelligence, Engineering, Manufacturing, Healthcare and Education. Dr. Aryal co-leads the Machine Learning for Decision Support (MLDS) Research Group at Deakin University and has secured over AUD 4.5 million in external research funding. His research is supported by diverse organizations including US and Australia Defence Agencies, the Australian Office of National Intelligence, Worksafe Victoria, the Victorian State Department of Education and Training, the Technology Innovation Institute (TII) UAE, and Table Tennis Australia (TTA). His notable awards include multiple Deakin University research and teaching awards, the Australian Postgraduate Award for his PhD studies, and several student travel awards during his doctoral candidature. Dr. Aryal actively supervises numerous PhD and Master's students and has contributed significantly to teaching in various courses at Deakin University and previously at Federation University. He serves on several university committees and contributes to the research community as a reviewer, program committee member, and editor for various journals and conferences.
Jacky Wai Keung is an Associate Professor in the Department of Computer Science at City University of Hong Kong with extensive industry connections across the Asia Pacific region. He leads the Artificial Intelligence and Software Engineering Research Group (AiSE) and serves as Chairman of IEEE Computer Society Hong Kong Chapter and Vice-President of Hong Kong STEM Education Alliance. Prof. Keung received his B.Sc.(Hons) in Computer Science from the University of Sydney and Ph.D. in Software Engineering from the University of New South Wales, Australia, before working as a Research Scientist at NICTA (now DATA61, CSIRO) in Sydney. His research spans software engineering, data science, AI, FinTech, machine learning, blockchain systems, and large language models for code generation and analysis. His recent work focuses on applying large language models to software engineering challenges, with publications examining code translation, anomaly detection, and autonomous driving system testing. The research shows a strong trend toward practical applications of AI in software development processes, particularly in FinTech and autonomous systems domains. Among his numerous accolades, Prof. Keung has been named in Stanford's top 2% most highly cited scientists for both 2022 and 2023, received the President's Teaching Excellence Award in 2020, and earned multiple IEEE best paper awards. His editorial service includes roles as Area Editor for Journal of Systems and Software since 2017 and Associate Editor for Information and Software Technology since 2020. Prof. Keung has successfully secured over HK$20 million in research funding through GRF, ITF, and TDG grants, including major projects like 'Smart Intelligent Process Automation for the Mortgage Lending Industry' (HK$2.62 million) and 'Software Data Analytics and Blockchain Technological Advancements' (HK$6 million). His industry collaborations have significantly enhanced student opportunities, with CS student starting salaries increasing by over 15% year-on-year for the past three years. He currently leads multiple research initiatives including RealisticCodeBench for evaluating LLMs in code generation and FedLAD for federated log anomaly detection, with several active projects focused on AI-enhanced InsurTech systems and deep probabilistic reasoning using deep learning.
Seung Yeob Shin serves as a Research Scientist at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT), where he contributes to the Software Verification and Validation (V&V) Lab under Prof. Lionel Briand. His active participation in major software engineering conferences includes serving on ASE 2025's Research Papers Program Committee and authoring multiple journal-first publications presented at premier venues like ICSE and ESEC/FSE. Shin earned his PhD in 2016 from the Laboratory for Advanced Software Engineering Research (LASER) at the University of Massachusetts Amherst's College of Information and Computer Sciences. His research expertise centers on applying formal methods to complex software systems, with particular emphasis on model-based verification techniques. His current research program bridges theoretical modeling and practical system validation across critical domains. Key focus areas include developing simulation frameworks for software-defined networks, creating model-checking approaches for cyber-physical control loops, and establishing probabilistic methods for real-time system verification. This work consistently targets reliability challenges in safety-critical infrastructure through rigorous engineering methodologies. Recent publications demonstrate a cohesive trajectory in applying formal verification to emerging system paradigms. The 2024 journal-first papers reveal increasing sophistication in handling non-deterministic behaviors across networking, embedded systems, and requirements engineering domains, with notable emphasis on failure induction and probabilistic safety guarantees. No scientific awards were documented in the available materials. While conference contributions indicate significant scholarly engagement, no information regarding student advising or research grants appears in the provided documentation. As a core member of the V&V Lab at SnT, Shin contributes to Luxembourg's national cybersecurity research infrastructure. The lab specializes in developing mathematical frameworks for system validation, with current projects addressing verification challenges in autonomous systems, critical infrastructure, and secure communications protocols through model-driven approaches.
Sophie Huiberts is a CNRS researcher at LIMOS, Clermont Auvergne University in Clermont-Ferrand since fall 2023. Previously, she was a Simons Junior Fellow at Columbia University in New York City, hosted by Tim Roughgarden. She completed her PhD research at Centrum Wiskunde & Informatica in Amsterdam under Daniel Dadush and received her doctorate in 2022 from Utrecht University. Dr. Huiberts specializes in theoretical aspects of mathematical optimization, particularly focusing on the gap between practical performance and theoretical predictions of linear programming algorithms. Her research examines software implementations like Gurobi, CPLEX, SCIP, and HiGHS to understand why these algorithms perform better in practice than worst-case analysis would suggest. She has made significant contributions to smoothed analysis of the simplex method, establishing both upper and lower bounds on its complexity under perturbations of worst-case inputs. Analysis of her publication record shows consistent focus on bridging theoretical computer science with practical optimization methods. Her work spans linear programming theory, integer programming, combinatorial optimization, and computational geometry, with particular emphasis on understanding the geometric properties of optimization problems and the behavior of algorithms on real-world instances. Simons Junior Fellowship Dr. Huiberts maintains active engagement with the research community through social media platforms including Mastodon and Bluesky, and produces high-quality recordings of her research talks available on YouTube. She has made a conscious decision to stop air travel since 2023 due to climate concerns, demonstrating commitment to sustainable research practices while maintaining scientific connections through digital means. She is affiliated with LIMOS (Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes), a research laboratory at Clermont Auvergne University focused on computer science, modeling, and optimization systems, where she continues her investigations into the theoretical foundations of practical optimization algorithms.
Michael Carbin is an Associate Professor at MIT in the Department of Electrical Engineering and Computer Science (EECS), where he leads the Programming Systems Group at the Computer Science and Artificial Intelligence Laboratory (CSAIL). His research centers on developing programming systems that handle uncertainty through probabilistic programming, quantum computing, and neural networks. Carbin's work spans programming languages, systems, and machine learning, with themes including uncertainty management, efficiency optimization, and formal verification. His publications demonstrate a strong focus on probabilistic inference methods, neural network optimization, and quantum programming frameworks. Awards and Honors: Sloan Research Fellowship (2020) Multiple Best Paper Awards (OOPSLA 2013, 2014; ICLR 2019) NSF CAREER Award (2018) Google Faculty Research Award (2018) As the head of the Programming Systems Group, he advises 10+ graduate students and postdocs, focusing on cutting-edge systems research. He has secured grants including Facebook Research Awards and NSF funding.
Eddy Keming Chen is an associate professor of philosophy at the University of California, San Diego (UCSD), affiliated with the John Bell Institute for the Foundations of Physics and UCSD's Chinese Studies Program. His work bridges philosophy of physics, metaphysics, and formal epistemology, with a focus on quantum foundations, time asymmetry, and nomic vagueness. He earned a PhD in philosophy (2019) and M.Sc. in mathematical physics (2019) from Rutgers University, alongside a graduate certificate in cognitive science. Research interests include laws of nature, quantum mechanics in time-asymmetric universes, and the metaphysics of the wave function. Notable works include Fundamental Nomic Vagueness (Philosophical Review, 2022) and Quantum Mechanics in a Time-Asymmetric Universe (BJPS Popper Prize, 2021). He is Co-PI of a Templeton grant exploring quantum foundations and has contributed to public philosophy via articles in New Scientist and interviews with the APA Blog. Teaching spans Chinese philosophy, symbolic logic, metaphysics, and philosophy of physics. Grants include UCSD Senate awards (2021–2025) and a $325k subaward from a $2.5M Templeton grant. Awards include the APA Public Philosophy Prize (2024) and Rutgers’ Harvey Waterman Medal (2019). Education: PhD in Philosophy, Rutgers University (2019) M.Sc. in Mathematics (Mathematical Physics), Rutgers (2019) Graduate Certificate in Cognitive Science, Rutgers (2018) B.S. Mathematics & B.A. Philosophy (Highest Honors), Calvin College (2013) His work on quantum foundations has been featured in Nature and Scientific American , with ongoing projects on surreal decision theory and Xunzi's meta-ethics. Current collaborations include a screenplay about time-travel romance inspired by SEP articles.
