Richard Dansereau is a Professor and Associate Dean (Graduate Studies) in the Department of Systems and Computer Engineering at Carleton University, part of the Faculty of Engineering and Design. He holds a Ph.D. from the University of Manitoba and is a Professional Engineer (P.Eng.) and Senior Member of IEEE. His research focuses on signal processing, including biomedical applications, compressive sensing, medical imaging, and fractal complexity analysis. He has led the Signal Processing and Machine Learning Lab and advised numerous PhD students in areas like PET image reconstruction and drone detection through Riemannian geometry. His academic roles include serving as Clerk of Senate and Academic Editor for IET Signal Processing. Key research contributions span deep learning for compressive sensing (e.g., DEQ-based networks), cervical cell segmentation, and biomedical signal processing. Over 150 publications highlight his work on topics like PET-MRI fusion, audio-visual speech enhancement, and radar systems. Collaborations include projects on cardiac PET imaging and drone detection algorithms. Awards include the IEEE Senior Member designation. His teaching spans courses such as Digital Signal Processing, Wavelets, and Biomedical Systems across Carleton and the Georgia Institute of Technology.
Professor Duncan Stewart is the Academic Head of Research at London Metropolitan University’s School of Social Sciences and Professions. He holds a professorship in Applied Health Research with a focus on substance use disorders, ageing populations, and multimorbidity. His work examines alcohol consumption patterns in older adults with chronic conditions and evaluates healthcare service delivery for individuals impacted by drug or alcohol misuse. Research Interests : Stewart’s research spans service evaluation, intervention development, and policy analysis related to alcohol and drug treatment. His current projects include the CHAMP-1 study exploring alcohol use in clinical pharmacist consultations and the integration of alcohol screening into primary care. He employs diverse methodologies such as randomized controlled trials, qualitative interviews, and systematic reviews to address gaps in healthcare provision. Teaching : Module leader for the Introduction to Epidemiology course in the Public Health MSc program. Grants & Projects : Currently leads the CHAMP-1 project funded by the National Institute of Health Research, focusing on alcohol identification during medication reviews. Past grants include studies on benzodiazepine dependence, pandemic impacts on cancer care, and barriers to pulmonary rehabilitation access. Labs/Teams : Directs research teams investigating alcohol policy innovations, clinical pharmacist roles, and geriatric health interventions. Collaborates with national institutions like NHS England and Scottish healthcare systems.
Vân Anh Huynh-Thu is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Liège (Belgium). Her research focuses on improving machine learning techniques with an emphasis on model interpretability. She is based at B28: Systems and Modeling in Quartier Polytech, Allée de la Découverte 10, 4000 Liège, Belgium. Her primary research interests span Machine Learning , Bioinformatics , and Gene Regulatory Network Inference . Dr. Huynh-Thu has developed several influential methods including GENIE3, dynGENIE3, and Jump3 for inferring gene regulatory networks from expression data. Her work bridges the gap between machine learning theory and biological applications, particularly in understanding complex disease mechanisms through computational approaches. She has made significant contributions to interpretable machine learning models that maintain high predictive accuracy while providing insights into feature importance and model behavior. Her research demonstrates a progression from purely computational methods toward translational applications in medical research. Analysis of her recent publications reveals a clear trajectory from foundational work on gene regulatory network inference toward broader applications in medical research, particularly in Crohn's disease. Her research increasingly integrates machine learning with clinical applications, demonstrating a shift from purely computational methods to translational research with direct medical implications. The consistent emphasis across her work is on interpretability, rigorous validation, and the application of tree-based methods to complex biological systems, with a growing focus on proteomics and biomarker discovery for inflammatory bowel diseases. Dr. Huynh-Thu maintains an active GitHub presence with implementations of her methods, demonstrating her commitment to open science and reproducibility. Her software repositories have garnered significant attention from the research community, with GENIE3 alone having 88 stars and 37 forks on GitHub. She has developed multiple implementations of her algorithms in Python, MATLAB, and R, making them accessible to researchers across different computational environments. Her work has been influential in the DREAM challenges, where GENIE3 was the best performer in two network inference competitions.
Hemanshu Kaul is an Associate Professor of Applied Mathematics at Illinois Institute of Technology (IIT), part of the College of Computing. He serves as Co-Director of the M.S. in Computational Decision Science and Operations Research (CDSOR) program. His expertise spans Discrete Mathematics, Operations Research, Graph Theory, and Network Optimization, with applications in transportation, computer science, and engineering. Education: PhD in Mathematics from the University of Illinois at Urbana-Champaign (UIUC), MS in Mathematics from the Indian Institute of Technology Bombay. He has held roles including Distinguished Teaching Fellow (2016–2018) and AMS Project NExT Fellow (2007–2008). Research Interests : Focus on Graph Packing, DP-coloring, List Coloring, and algorithmic solutions for discrete optimization problems. His work bridges theoretical foundations with practical applications such as transportation networks and computer science systems. Publications & Grants : Over 50 publications in combinatorics and optimization, including NSF/NSA-funded projects like the EXCILL III Conference (2016–17). Recent work explores spectral Turán problems, DP-coloring algorithms, and longitudinal network models. Awards : Board of Trustees Award for Excellence in Teaching (2019) Excellence in Teaching Award (2017, College of Science, IIT) Interdisciplinary Research Grant (2009–2010, Transportation Networks) Advising & Leadership : Co-advisor for IIT's SIAM Student Chapter. Led restructuring of the Applied Math M.Sc. program (2018–19). Advised teams in the Mathematical Contest in Modeling (MCM), including a 2019 Meritorious Winner team for a disaster response system design. Labs & Collaborations : Involved in interdisciplinary projects combining applied math with computer science and engineering, including work on equitable public transit systems and network optimization.
Igor Molybog is an Assistant Professor at the University of Hawai'i at Manoa, holding joint appointments in the Departments of Electrical and Computer Engineering and Information and Computer Sciences. His research focuses on advancing artificial intelligence, particularly through large language models (LLMs), multimodal modeling, and core machine learning optimization. He leads the HawAII research group, exploring applications like LLM alignment, efficient inference systems, and scaling properties of foundation models. Education: Ph.D. in Engineering from UC Berkeley (2022), specializing in optimization algorithms for complex systems. Previously worked at Meta AI on LLaMa model development. Research Interests: Efficient LLM development and evaluation frameworks Multimodal AI integration (video/audio + text) Scalable optimization for large models Computational efficiency in training/ inference Recent Work: Presented REAL alignment method (2024), developed long-context scaling techniques (2023), contributed to Llama 2 chat models (2023). Collaborates with organizations like Epoch AI on scaling challenges. Teaching: Offers courses in AI, machine learning, and optimization across ECE and ICS departments. Labs/Teams: Leads HawAII Initiative fostering AI collaboration at UH Manoa, organizes paper reading seminars, and hosts technical talks with industry experts.
Joel E. Cohen is the Abby Rockefeller Mauzé Professor at The Rockefeller University, where he leads the Laboratory of Populations. With over five decades of research experience, Cohen has pioneered innovative mathematical approaches to study biological populations and variability. His work bridges mathematics, biology, and environmental science, fundamentally changing how scientists understand population dynamics and the significance of biological variability. Dr. Cohen's research focuses on developing new mathematical tools to address population problems in demography, epidemiology, and ecology. He has made seminal contributions to the understanding of heavy-tailed distributions that describe extreme events like hurricanes and disease outbreaks, challenging traditional statistical approaches. His laboratory has conducted groundbreaking research on the spatial distribution of human populations in relation to geophysical factors, with unexpected practical applications ranging from soap formulation to semiconductor manufacturing. Cohen has also developed mathematical models for Chagas disease control in rural Argentina and created algorithms to predict international migration patterns. Analysis of Cohen's recent publications reveals a sustained focus on Taylor's law of fluctuation scaling, population dynamics, and ecological statistics. His work consistently demonstrates how abstract mathematical concepts can transform our understanding of biological systems, from cellular processes to global population trends. The research spans theoretical mathematics to practical applications in disease control, conservation biology, and environmental management. Olivia Schieffelin Nordberg Prize for excellence in writing in the population sciences (March 1997) Gheorghe Lazar Prize of Romanian Academy (December 2000) As director of the Laboratory of Populations, Cohen has led research on human population growth, infectious diseases, food webs, and international migration. His methods for assessing the uncertainty of population projections have been applied in court cases for predicting future claimants of asbestos-related diseases. Cohen's laboratory has collaborated with the United Nations Population Division on migration studies and developed mathematical models that account for more than half of the variability in annual migration numbers among 229 countries. Current research directions include understanding how demographic, economic, and cultural changes interact with Earth's physical, chemical, and biological environments. The Laboratory of Populations employs a multidisciplinary approach that combines mathematical modeling, statistical analysis, and field studies to address complex population issues. Their work exemplifies how basic quantitative research on populations frequently yields unexpected practical applications, demonstrating the profound connections between theoretical mathematics and real-world challenges in public health, environmental science, and resource management.
Dr. Zhonghua Zheng is an Assistant Professor (tenured) in Data Science & Environmental Analytics at the Department of Earth and Environmental Sciences, The University of Manchester. He leads the Manchester Environmental Data Analytics Lab (MEDAL) and serves on editorial boards of journals like Atmospheric Chemistry and Physics . His research focuses on data-driven approaches to urban climate modeling, aerosol science, and environmental systems analysis. Education : PhD in Environmental Engineering (UIUC, 2020) MCS in Computer Science (UIUC, 2022) MS in Agricultural & Biological Engineering (UIUC, 2016) BEng in Biosystems Engineering (Zhejiang University, 2015) Research Interests : Dr. Zheng integrates data science (AI, ML) with Earth system modeling to address challenges in urban climate adaptation, air quality, and agricultural-environmental nexus. Key themes include: Urban heat stress projections using machine learning Aerosol mixing state characterization via foundation models Climate adaptation strategies for net-zero cities Agricultural emissions modeling in Southeast Asia Recent Article Trends : His 2025 publications emphasize machine learning applications in urban climate and aerosol science, including pollen fragmentation dynamics and black carbon mixing effects. Earlier work (2024) focuses on model improvements for urban surface albedo and gas-phase chemistry. Awards : 2025: Aerosol Science Career Development Grant 2024: Best Student Poster Award (AOGS) 2023: City Brain Open Research Youth Scholar Advising & Grants : Supervises 10+ PhD students (e.g., Yuan Sun, Fei Jiang) and has secured grants including a Royal Society AI + Air Quality award. Leads interdisciplinary projects with NERC, The Alan Turing Institute, and Yunqi Academy. Labs & Teams : Runs MEDAL lab collaborating with global institutions (MIT, NCAR, Columbia). Active in initiatives like UK Atmospheric Chemistry Conference and Open Data Computing Workshop.
David Spiegelhalter is Professor of the Public Understanding of Risk at the University of Cambridge's Faculty of Mathematics. His research focuses on Bayesian statistics, public communication of uncertainty, biostatistics, and performance assessment in healthcare. He has contributed extensively to risk perception studies, particularly during the COVID-19 pandemic, and develops tools for statistical communication. Spiegelhalter's work bridges technical statistics with public understanding, emphasizing transparent communication of scientific uncertainty. His research interests include: Developing frameworks for trust in scientific advocacy Quantifying pandemic risks and public responses Creating accessible statistical tools for healthcare decisions His publications show consistent focus on: Public health communication strategies Philosophical foundations of probability Epidemiological modeling with recent emphasis on COVID-19 data interpretation.
Laura Kwong is an Assistant Professor in the Division of Environmental Health Sciences at the UC Berkeley School of Public Health, specializing in environmental justice and global health equity. Her work focuses on designing and evaluating engineering interventions to address health disparities in low-income countries, particularly regarding child/maternal health, air pollution, and infectious diseases. She employs community-based participatory research methods in Bangladesh, Uganda, Indonesia, China, and other regions. Dr. Kwong holds a PhD in Civil & Environmental Engineering from Stanford University, with prior experience as an ORISE Fellow at the U.S. EPA. Her interdisciplinary research integrates qualitative and quantitative methods, including randomized-controlled trials and sensor-based assessments. Key areas include household-level interventions to reduce environmental contamination, fecal exposure mitigation, and climate-resilient sanitation solutions. Her recent publications highlight community-driven solutions for energy access, fecal pathogen transmission pathways, and the impact of weather extremes on health. She leads the Global Environmental Health Equity Lab, emphasizing human-centered design in public health infrastructure. Dr. Kwong’s work bridges engineering and public health to advance equity in resource-poor settings, with a focus on interventions that address both health and environmental sustainability.
Prof. Robert Grass is a Lecturer at the Department of Chemistry and Applied Biosciences at ETH Zurich, affiliated with the Institute for Chemical and Bioengineering Sciences. His research focuses on innovative applications of nanotechnology, DNA-based storage systems, and sustainable catalytic processes for CO2 valorization. Grass has pioneered silica-encapsulated DNA technologies for traceability in healthcare, environmental monitoring, and anti-counterfeiting measures. His work bridges chemical engineering with information technology, addressing challenges in long-term data preservation and molecular-level security. Current projects include developing compostable DNA storage materials and designing catalysts for methanol synthesis from CO2, contributing to both environmental sustainability and energy systems. Grass's interdisciplinary approach integrates nanomaterials design, enzymatic processes, and machine learning to advance next-generation storage and sensing technologies. Research Interests: Development of DNA-based storage systems with error-correction mechanisms Nanoparticle engineering for medical and environmental applications Catalytic materials for CO2 conversion and green chemistry Bio-inspired security systems using molecular randomness Sustainable materials for long-term data preservation His recent work highlights advancements in silica-encapsulated DNA tracers for tracking pathogen transmission dynamics, as well as low-nuclearity catalysts enabling efficient methanol synthesis from CO2. Grass actively explores the intersection of nanotechnology and digital information, including cryptographic applications leveraging DNA's inherent complexity.
Natesh Pillai is a Professor in the Department of Statistics at Harvard University and a Distinguished Engineer at LinkedIn, focusing on Responsible AI. He holds a Bachelors from IIT Madras, a PhD from Duke University's Department of Statistical Science (2008), and completed a postdoc at the University of Warwick's CRiSM (2008-2010). His research spans applied probability, computational methods, MCMC theory, algorithmic fairness, and climate science. He serves on editorial boards for journals like SIAM Journal on Mathematics of Data Science and Harvard Data Science Review . Key awards include the 2018 Young Statistical Scientist Award and 2021 Fellowship in the Institute of Mathematical Statistics. His work emphasizes bridging theory and practice, with contributions to statistical methodology, causal inference, and scalable computational techniques. Recent collaborations include industry roles at Amazon (2021-2023) and interdisciplinary climate science projects analyzing agricultural yield predictability. Education: Bachelor's: Indian Institute of Technology (IIT) Madras PhD: Duke University, Department of Statistical Science Research Focus: MCMC mixing times, Bayesian methodology, reinforcement learning, climate data modeling Publications emphasize algorithmic efficiency, fairness in AI, and probabilistic frameworks for complex systems. His lab integrates theoretical rigor with real-world applications, including climate modeling and healthcare analytics.
Roland Bauerschmidt is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He previously held a professorship at the University of Cambridge and conducted postdoctoral research at Harvard and the Institute for Advanced Study. His research focuses on probability theory, mathematical physics, and statistical mechanics, particularly in spin systems, renormalization group methods, stochastic dynamics, and random matrices. He has contributed extensively to understanding phase transitions, critical phenomena, and universality classes. Education: PhD from the University of British Columbia (advised by David Brydges and others), undergraduate degrees from ETH Zurich. Research interests include applications of supersymmetry in probability, Coulomb systems, and log-Sobolev inequalities. He has organized programs such as the HIM Trimester Program on Probabilistic Methods in Quantum Field Theory. His teaching spans graduate and undergraduate courses in probability theory, stochastic dynamics, and mathematical analysis at NYU, Cambridge, and international institutions. His work frequently intersects with renormalization group techniques, spectral gaps, and non-equilibrium dynamics. Recent publications explore log-Sobolev inequalities in spin systems, high-temperature discrete Gaussian models, and percolation transitions in random forests.
Prem Kumar is a Professor in the Department of Physics at Swansea University, holding a Personal Chair position. He is affiliated with the Particle Physics and Cosmology Theory (PPCT) group. His research focuses on the intersection of Quantum Field Theory and String Theory/Gravity, including specialized areas like gauge/gravity duality and supersymmetric Yang-Mills theories. Kumar earned his undergraduate degree in Electrical Engineering from the Indian Institute of Technology Madras and a PhD in Theoretical Physics from Carnegie Mellon University. He held postdoctoral positions at the University of Washington and Cambridge University before joining Swansea under a 5-year PPARC Advanced Fellowship. He has been a faculty member since 2005 and Professor since 2012. His research employs analytical and computational methods to explore quantum phenomena in high-energy physics, thermal field theories, and random matrix applications in theoretical frameworks. Recent publications analyze atmospheric physics and climate extremes, with recurring themes in synoptic-scale precursors to extreme weather and climate model downscaling. Scientific awards include the prestigious PPARC Advanced Fellowship and Personal Chair recognition. Kumar actively advises postgraduate students and contributes to theoretical physics research groups exploring quantum-gravitational interactions.
Kaiyu Hang is an Assistant Professor of Computer Science at Rice University, directing the Robotics and Physical Interactions Lab (RobotΠ Lab). He holds a PhD and MSc from KTH Royal Institute of Technology and a B.Eng. from Xi’an Jiaotong University. His postdoctoral research was conducted at Yale University. His research focuses on robotic systems capable of physically interacting with the environment and humans, emphasizing algorithms in optimization, learning, and control. Key areas include manipulation systems (small-scale grasping to large-scale multi-robot manipulation), robust control, and energy-efficient UAV perching mechanisms inspired by nature. His work has been featured in MIT Technology Review, Science Robotics, and NPR. Hang has received notable awards such as the NSF CAREER Award (2023) and ASME Rising Star (2024). He serves on editorial boards for IEEE Robotics and Automation Letters (2019–present), ICRA (2021–present), IROS (2020–present), and Humanoids (2019). He also organizes the 10th Robotic Grasping and Manipulation Competition (RGMC) at ICRA 2024. As a faculty advisor for the Rice Robotics Club and on the CS Graduate Admission Committee, Hang actively mentors students and promotes inclusivity in robotics through initiatives like Inclusion@RSS. His lab’s projects aim to enhance manipulation robustness, develop novel UAV landing gear, and advance nonprehensile manipulation via motion planning and control.
Catherine Wolfram is a Gibbs Assistant Professor at Yale University and a National Science Foundation (NSF) postdoctoral fellow. She holds a PhD in Mathematics from MIT (2024), advised by Scott Sheffield, and an undergraduate degree from the University of Chicago. Her research focuses on complex analysis, probability, and geometry, with applications to statistical mechanics and random surfaces. She has presented her work at institutions including the University of Michigan, Columbia University, and international conferences such as the World Congress on Probability and Statistics. Her recent activities include talks on dimer models, holography in mathematical physics, and Teichmüller theory. Catherine has also engaged in teaching roles at MIT, leading recitations in probability and multivariable calculus. Her research has been supported by NSF postdoctoral funding, and her work has been published in journals like International Mathematics Research Notices (IMRN). She actively collaborates with researchers in probability, geometry, and mathematical physics, evidenced by her co-authored papers and participation in workshops at IHES, CIRM, and the Fields Institute.
