Kyle W. Klarich is Professor of Medicine and consultant in both the Division of Structural Heart Disease and Division of Echocardiography at Mayo Clinic. His clinical practice and research focus on structural heart disease, cardiac tumors, hypertrophic cardiomyopathies, and valvular heart disease. Dr. Klarich investigates complications prevention and quality-of-life improvement for patients with rare cardiac conditions. As Cardiovascular Disease Fellowship program director since 2010, he is extensively involved in medical education and has received multiple teaching awards including the ACGME's Parker J. Palmer Courage to Teach Award finalist recognition.
Inna Fishman, Ph.D., is a Research Associate Professor at San Diego State University's Department of Psychology within the College of Sciences. Her research investigates brain network organization in autism spectrum disorder (ASD) using multimodal MRI techniques, focusing on developmental trajectories from toddlerhood to adulthood. She directs studies on sensory processing, socioeconomic influences, and neural connectivity patterns in ASD. Research Focus: Dr. Fishman's work bridges social neuroscience and clinical neuropsychology, examining: Early biomarkers of ASD via functional/diffusion MRI Impact of bilingualism and socioeconomic factors on neurodevelopment Sleep disorders and sensory sensitivities in autistic children Aging-related neural changes in adults with ASD Publication Trends: Her recent articles (2021-2025) emphasize: 1) Advanced neuroimaging of ASD across lifespan stages, 2) Machine learning applications for diagnostics, 3) Socioeconomic and environmental modulators of brain development, and 4) Sleep/auditory processing comorbidities. Student Advising & Grants: She mentors doctoral candidates (Lindsay Olson, Jiwandeep Kohli, Bosi Chen) and leads NIH-funded projects including a clinical psychology fellowship for autism evaluation across ages. Laboratory Affiliation: Dr. Fishman co-directs the Brain Development Imaging Laboratories (BDIL), which investigates ASD manifestations through behavioral and neuroimaging approaches.
Dr. Arpan Man Sainju is an Assistant Professor and Internship Coordinator in the Department of Computer Science at Middle Tennessee State University (MTSU). He holds a PhD (2021) and MS (2020) from the University of Alabama, and a B.E. (2011) from Tribhuvan University. His research focuses on spatial big data analytics, spatiotemporal data mining, and GIS applications in environmental modeling, disaster management, and geospatial science. He develops innovative algorithms for Earth imagery segmentation, flood inundation mapping, and physics-aware machine learning models. Education: PhD in Computer Science, University of Alabama (2021) MS in Computer Science, University of Alabama (2020) B.E. in Computer Science, Tribhuvan University (2011) Key research interests include deep learning for geospatial tasks, semi-supervised learning with limited labels, and parallel computing for big spatial data. His work bridges computer science and environmental science, addressing challenges in hydrology, urban safety, and disaster response. He has published extensively in top journals like ACM TIST, IEEE TKDE, and Environmental Modelling & Software, focusing on applications like flood modeling, road safety analysis, and 3D shape analysis. Dr. Sainju collaborates on interdisciplinary projects involving physics-guided models, hidden Markov structures, and GPU-accelerated algorithms. His research has been applied to real-world scenarios such as hurricane flood analysis and malware detection through Windows log analysis.
Johanna Ziegel is a Professor of Statistics at ETH Zurich, Switzerland, since 2024, and a Visiting Scientist at the Heidelberg Institute for Theoretical Studies (HITS). Previously, she held positions at the University of Bern, where she was promoted to Full Professor in 2023. Her research focuses on decision-theoretically sound methods for forecast evaluation, probabilistic forecasting, risk measures in finance, and applications in meteorology, medicine, and climate science. She is actively involved in editorial roles for journals like Bernoulli , JASA: Theory & Methods , and SIAM Journal on Financial Mathematics . Education: PhD in Stereological Analysis of Spatial Structures from ETH Zurich (2010), supervised by Paul Embrechts and Eva B. Vedel Jensen. Postdoctoral research at the University of Melbourne and Heidelberg University. Research Interests: Forecast evaluation, elicitable functionals, risk measures, isotonic regression, statistical calibration, and applications in finance, climate science, and biostatistics. Her work bridges theoretical statistics with practical challenges in uncertainty quantification and decision-making under uncertainty. Advising & Collaborations: Supervised 7 PhD students and mentored several postdocs. Collaborates with the Computational Statistics group at HITS and the Oeschger Centre for Climate Change Research. Her group explores distributional regression under order constraints and novel methods for forecast comparison. Recognition: Credit Suisse Award for Best Teaching (2022), H.I.T. Program for Academic Leadership (2021–2022). Active in professional service, including the Bernoulli Society Council and editorial boards.
Thierry Warin is a Full Professor of Data Science for International Business at HEC Montréal, directing the Department of International Business. He holds the Professorship in Data Science for International Business and is a Principal Investigator at CIRANO, leading the World Economy theme. His roles include affiliations with Harvard Business School’s Microeconomics of Competitiveness program and the International Trade and Finance Association presidency (2020-2022). Education: PhD from ESSEC Business School (France, 2000). Professional development includes the Harvard Business Analytics Program (2018-2020) and GIS training at Harvard. Research Interests: Data science applications in global economic transformations, including network theory, natural language processing, and computational methods. Focus areas: algorithmic collusion, platform economies, and metadata-driven analyses. He develops open-source tools like the statcanR package and advocates for reproducible research. Articles Trends: Recent work explores AI regulation, algorithmic competition, climate transition plans, and central bank speech analysis. Methodologies span structural topic modeling, social media analytics, and entropy-based frameworks. Awards: Honored with the Highly Commended Paper Award (2017-2018) and Emerald Literati Award (2018) for reverse innovation research. Recognized for contributions to computational social science and regulatory frameworks. Advising & Grants: Supervised 16 master’s projects since 2019, focusing on data science applications in global business challenges. Active in interdisciplinary initiatives like the St. Lawrence–Great Lakes corridor data hub. Labs & Philanthropy: Founded quantum simulations and leads Ed’Haîti , an NGO addressing education in Haiti. Collaborates on Science des données au féminin en Afrique , empowering 200 African women with data science skills.
Dr. Nilanjan Banerjee is a Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He leads the Mobile, Pervasive, and Sensor System Lab, focusing on embedded and distributed systems for mobile, pervasive, and sustainability-based computing. His research spans renewable energy-driven systems, health diagnostics, mobile usability, and experimental testbed design. He holds a Ph.D. in Computer Science from the University of Massachusetts (2009), an M.S. from the same institution (2007), and a B.Tech. (Hons) from the Indian Institute of Technology (2004). Dr. Banerjee's work emphasizes interdisciplinary innovation, including low-power wearable devices for health monitoring (e.g., RestEaZe), cybersecurity frameworks for embedded systems (e.g., CARE), and sensor-based solutions for environmental sustainability. His contributions address challenges in mobility, energy efficiency, and accessibility, such as the Presight sidewalk localization system for visually impaired riders and the Inviz gesture-recognition textile sensors. His recent publications (2018–2021) reflect a focus on health technology, cybersecurity, and sustainable systems. Notable trends include: Integration of machine learning with sensor data for medical applications (e.g., sleep analysis, infection detection) Development of lightweight security protocols for embedded devices Exploration of renewable energy solutions for mobile and sensor networks No scientific awards are explicitly listed in the provided text. His academic advising and grant activities are not detailed here, but his lab's active research suggests significant collaborative projects. The lab also pioneers educational strategies in mobile app development and inclusive faculty recruitment through peer education programs like STRIDE.
Dr. Teresa Wang is a Senior Lecturer in Data Science at Monash University's Faculty of Information Technology, specializing in entity/user modeling, relational/structural machine learning, and graph/network analysis. She holds a Ph.D. from the University of Queensland and degrees from Nanjing University. Currently, she directs the Master of Data Science Program and teaches courses like FIT5201 Machine Learning. Her research focuses on social, e-commerce, and health data modeling, with notable projects including the Knowledge Enriched Approach for Effective Personalization (2025–2027) and collaborations on AI in Mental Health and Site Safety. Dr. Wang has co-authored over 59 publications, emphasizing areas like ontology matching and multimodal data analysis. She actively supervises PhD students and contributes to initiatives like the CSIRO Next Generation Graduates Program for clean energy and sustainability. Education: Ph.D. in Computer Science (2017), University of Queensland Master of Computer Science (2013), Nanjing University Bachelor of Software Engineering (2010), Nanjing University Research Interests: Entity modeling, spatio-temporal data analysis, graph mining, recommender systems, and health/medical records mining. She explores applications in social media, e-commerce, and healthcare sectors. Projects: "Knowledge Enriched Approach for Effective Personalization" (2025–2027) "AI for Clean Energy and Sustainability" (2023–2027) "CSIRO Next Generation Graduates Program: AI in Mental Health" (2023–2027) "Large-scale multimodal knowledge management" (2022–2025) Grants & Collaborations: Engaged with CSIRO, Crank Group, and Pola Practice Pty Ltd. Her work aligns with UN SDGs in education and sustainable energy systems. Labs/Teams: Part of the Monash Energy Institute and Monash Data Futures Institute, contributing to interdisciplinary AI and energy research.
Junier Oliva is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill and Lead Faculty of the Master of Applied Data Science program. His research focuses on machine learning, artificial intelligence, and nonparametric statistics, particularly in high-dimensional density estimation, sequential modeling, and learning from complex/structured data. He holds a B.S., M.S., and Ph.D. in Computer Science from Carnegie Mellon University, with prior industry experience at Yahoo! and Uber ATG. Research Interests: Machine learning, artificial intelligence, nonparametric statistics, deep learning, statistical data mining, signal processing, kernel methods, and scalability. His work bridges machine and human learning via collective approaches, emphasizing simple yet flexible models for massive datasets. Awards/Grants: $592K AIM-AHEAD/NIH Grant for Human+AI Collaboration $594K NSF Grant for Scientific Discovery $500K NSF Grant for 'Machine Detectives' Project ACM BCB Best Paper Award (2022) for transparent single-cell classification work Labs/Teams: Director of the LUPA Lab, which develops machine learning techniques for holistic data understanding across domains like healthcare, earth science, and computer vision.
Professor Anne Keegan is a Full Professor of International Human Resource Management at University College Dublin (UCD), leading the HRM and Employment Relations Group within the School of Business. She previously held roles at the Amsterdam Business School and Erasmus University Rotterdam, and completed her PhD at Trinity College Dublin. Her research focuses on HRM in online labor platforms, gig economies, project-based organizations, and institutional/paradox theories. Education: Bachelor of Arts (BA), Trinity College Dublin Doctor of Philosophy (DPhil), Trinity College Dublin Research Interests: HRM in digital labor platforms and gig economies Institutional complexity and paradox theory applications HR practices in project-based organizations Career dynamics in non-traditional work environments Awards: EGOS Award in Honour of Max Boisot (2014) Van der Schroeff Award for Best Lecturer (2013) Professional Activities: Editorial Board roles for International Journal of Human Resource Management and International Journal of Project Management Committee memberships: UCD Academic Council, Faculty Promotions Committee, Widening Participation initiatives Keynote speaker at NEON Annual Conference (2019) Grants: UCD School Funded Research Account (2024-2027) Labs/Teams: Leading research on algorithmic management, platform ecosystems, and gig economy ethics through collaborative international projects.
Oded Regev is a Silver Professor at the Courant Institute of Mathematical Sciences, New York University. He has previously held positions at Tel Aviv University and École Normale Supérieure, Paris (CNRS). His research spans theoretical computer science, cryptography, quantum computation, and machine learning applications in biological discovery. Education: PhD in Computer Science, Tel Aviv University (2001) Regev is renowned for his work in lattice-based cryptography, including the introduction of the Learning With Errors (LWE) problem and Gaussian measures. He also leads research using interpretable machine learning to decode RNA splicing logic and nuclear speckle dynamics, with implications in disease and therapeutics. His recent articles focus on quantum factoring, RNA localization, and geometric lattice bounds. Scientific Awards: European Research Council (ERC) Starting Grant (2008) 2018 Gödel Prize 2019 Simons Investigator Award Best Paper Awards: STOC 2003, Eurocrypt 2006 Regev mentors students and postdocs in both theoretical computer science and computational biology. His lab has secured funding from NSF, NIH, and Additional Ventures. He co-founded the TCS+ online seminar series and serves as Associate Editor-in-Chief for Theory of Computing .
Arthur Bousquet is an Associate Professor of Mathematics at Lake Forest College, affiliated with the Math and Computer Science department. He holds a PhD in Applied Mathematics from Indiana University (Bloomington, IN) and a MS in Engineering in applied mathematics and scientific computing from SuP Galilee Engineering School (Paris, France). His research focuses on numerical methods for partial differential equations, including finite volume and finite element techniques, with applications to geophysical fluid dynamics, climate modeling, and biomedical problems like viral shell mechanics. Notable areas include shallow water equations, phase field modeling, and computational methods for atmospheric dynamics. Bousquet has published extensively on topics such as numerical weather prediction, electrokinetic equations, and virus nanoindentation modeling. His work often combines theoretical analysis with computational simulations to address complex systems in fluid dynamics and materials science. He has received the Rothrock Award for teaching excellence (2014) and held research fellowships including an NSF Graduate Fellowship (2009-2013). His teaching includes courses like Computational Mathematics, Multivariable Calculus, and Real Analysis.
Gauthier Gidel is an Associate Professor at the Department of Computer Science and Operations Research (DIRO) within the Faculty of Arts and Science at Université de Montréal, where he also holds the prestigious Canada CIFAR AI Chair position. He is a core faculty member of Mila, Quebec's AI research institute, and maintains active research collaborations with leading institutions. His academic journey includes a PhD in Computer Science under the supervision of Simon Lacoste-Julien, with internships at Sierra, ElementAI, and DeepMind during his doctoral studies. Dr. Gidel's research spans multiple critical areas in machine learning, with particular emphasis on generative modeling , adversarial machine learning , and variational inequalities for machine learning. His work explores the intersection of optimization theory and practical AI systems, focusing on challenges like LLM safety alignment, multi-agent cooperation, and robustness against adversarial attacks. He is particularly known for his contributions to understanding the theoretical foundations of generative adversarial networks through variational inequality frameworks. His recent publications reveal a strong trend toward addressing critical challenges in large language model safety and alignment, with numerous 2024-2025 papers focusing on adversarial robustness, safety evaluation methodologies, and alignment techniques for LLMs. Simultaneously, his foundational work continues in optimization theory, particularly in variational inequalities and performative prediction, demonstrating his dual focus on practical AI safety concerns and theoretical machine learning foundations. Canada CIFAR AI Chair Core member of Mila Organizer of popular NeurIPS workshops on smooth games Co-founder of the ICLR blog post track Dr. Gidel actively supervises an extensive research group with approximately 10 current graduate students and numerous alumni who have secured positions at leading institutions including Inria Lyon, Oxford, and industry research labs. His research is supported by multiple substantial grants from CRSNG, MITACS, and IVADO, including the prestigious CRSNG Discovery Grant program and MITACS Acceleration Québec projects focused on fraud detection in music streaming and conditional generation. His laboratory maintains strong connections with both academic and industry partners, fostering a collaborative environment focused on advancing AI safety and theoretical understanding.
Dr. Timothy Fraser is a Computational Social Scientist serving as Ezra Systems Research Associate in Cornell University's Systems Engineering Program and Coordinator for the Center for Transportation, Environment, & Community Health (CTECH). Holding a PhD in Political Science from Northeastern University (2022), he focuses on climate change adaptation, disaster resilience, and energy policy using big data analytics, GIS, and AI. His work bridges computational methods with societal challenges, emphasizing community engagement and policy impact. Education: PhD in Political Science, Northeastern University (2022) MA in Political Science, Northeastern University BA in International and Global Studies, Middlebury College Research Interests: Fraser’s research integrates computational social science with environmental policy, exploring how social networks and governance structures influence cities' climate adaptation strategies. Key areas include renewable energy adoption, disaster evacuation dynamics, and pandemic response mechanisms. He employs mixed methods such as network analysis, statistical modeling, and fieldwork. Grants & Awards: Fulbright Fellowship (2016), Kyushu University Japan Foundation Doctoral Fellowship (2020) USDOT Multimillion Grant Administrator (2022-2023) Teaching & Mentorship: Fraser teaches statistical methods and research design at Cornell, advising over 30+ students in projects published in leading journals. He coordinates capstone teams and mentors researchers in data science and policy analysis. Labs & Projects: Leads the Climate Action in Transportation dashboard initiative (Gao Labs), developing tools for emissions visualization. Collaborates on UNDP social capital mapping projects in Mexico and Paraguay, applying spatial analysis techniques for vulnerable communities.
Guido Montúfar is a Professor in the Departments of Mathematics and Statistics & Data Science at the University of California, Los Angeles (UCLA), effective since 2024. He also leads the Mathematical Machine Learning Group at the Max Planck Institute for Mathematics in the Sciences (MPI MIS) in Leipzig, Germany since 2018. His academic journey includes a PhD in Mathematics from Leipzig University (2012), and Diplom degrees in Physics and Mathematics from TU Berlin (2009 and 2007). Montúfar's research focuses on the theoretical foundations of deep learning, mathematical machine learning, and the interplay between geometry and learning. Key areas include neural network architecture theory, optimization landscapes, and information geometry. His work bridges algebraic statistics, graphical models, and topological data analysis. His grants and awards include an ERC Starting Grant (2018-2023), a Sloan Research Fellowship (2022), and an NSF CAREER Award. He has advised numerous PhD students and postdocs, contributing to significant advancements in machine learning theory and applications. Montúfar teaches courses on applied mathematics, optimization, and machine learning at UCLA. His research also explores topics like oversquashing in graph neural networks and the geometry of policy gradients in reinforcement learning.
Loris D'Antoni is an Associate Professor in the Department of Computer Science and Engineering at the University of California at San Diego (UCSD) . He is also a Visiting Academic at Amazon Web Services (AWS) . His research focuses on helping people write trustworthy software through techniques in program synthesis, formal verification, and machine learning robustness. Bachelor and Master in Computer Science from University of Torino (2008, 2010) PhD in Computer Science from University of Pennsylvania (2015) His research integrates programming languages , automata theory , and formal methods to ensure software reliability. Recent work explores semantics-guided synthesis and specification-aligned LLMs , with applications in network security, machine learning fairness, and automated code repair. Key trends in his publications include program synthesis , formal verification , and trustworthy AI systems . He has contributed to tools like AutomataTutor and SemGuS , a framework for customizable synthesis problems using constrained Horn clauses. Phillip R. Certain-Gary D. Sandefur Distinguished Faculty Award NSF CAREER Award Microsoft Research Faculty Fellowship Google and Facebook Faculty Awards Best Paper Award at ICDCN 2023 Distinguished Paper Award at SBES 2021 D'Antoni actively contributes to academic community service as a committee member in PLDI , OOPSLA , POPL , and CAV . He leads the Programming Systems Group at UCSD and collaborates with SemGuS research team on synthesis frameworks.
