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Lucas B JansonView profile

Associate Professor

• High-Dimensional Inference
• Statistical Machine Learning
• Computational Statistics
• +3 more

Lucas B Janson is an Associate Professor of Statistics and Affiliate in Computer Science at Harvard University, located at Science Center #710. His primary research focuses on high-dimensional inference and statistical machine learning, with applications in genetics, political science, and climatology. He leads the Harvard Statistical Consulting Service, supervising PhD students advising hundreds of researchers annually. His teaching includes courses such as Statistical Inference I, Introduction to Reinforcement Learning, and Statistical Machine Learning. He has contributed to numerous software packages, including implementations of knockoff filters, floodgate variable importance, and the distilled conditional randomization test (dCRT). Key research themes include model-free variable importance, conditional independence testing, and safe reinforcement learning. His work emphasizes methodology with rigorous statistical guarantees, particularly in high-dimensional and complex data settings. Collaborations span interdisciplinary domains, leveraging statistical tools to address real-world challenges. He actively engages in advancing computational methods for statistical inference and their practical deployment in scientific research.

Mike EfthymiouView profile

Adjunct Professor

• Offshore Engineering
• Structural Reliability
• Oil & Gas Technology
• +4 more

Mike Efthymiou is an Adjunct Professor at the School of Earth and Oceans and the UWA Oceans Institute, University of Western Australia. Previously a Professor of Offshore Structures (2013–2022), his career spans over 30 years with Shell, where he pioneered FLNG technology and contributed to ISO standards in structural reliability and safety. He holds patents in offshore technologies and serves on the Board of Directors of Cyprus Hydrocarbons Company. Education: PhD in Civil & Structural Engineering (University of Manchester, UK). Research focuses on offshore structural integrity, mooring systems, tropical cyclone impacts, and fatigue analysis. His recent articles emphasize probabilistic frameworks for riser fatigue, FPSO mooring systems under extreme weather, and composite retrofitting of marine structures. Grants include ARC ITRH projects on offshore floating facilities (2014–2022). His work aligns with UN Sustainable Development Goals for sustainable infrastructure and climate action.

Jean-Marie DufourView profile

Professor

• Econometrics
• Macroeconomics
• Finance
• +7 more

Professor Jean-Marie Dufour holds the William Dow Chair in Economics at McGill University's Department of Economics and is a Research Professor at the Halle Institute for Economic Research (IWH). His research focuses on econometrics, macroeconomics, finance, and public finance, with a particular emphasis on dynamic models, policy analysis, and financial asset pricing. He has held prestigious roles such as Bank of Canada Research Fellow and Fellow of the Econometric Society. Education: PhD in Economics (University of Chicago, 1979), M.Sc. Mathematics (Université de Montréal, 1973) Affiliations: CIRANO, CIREQ, and invited researcher at IWH Major awards include the Killam Prize (2006), Officer of the Order of Canada (2008), and Fellowships from the Royal Society of Canada and the Econometric Society. His work bridges theoretical econometrics with applied macroeconomic and financial analysis. Editorial roles include Associate Editorships at Econometrica and Journal of Econometrics . His research outputs span structural equation modeling, causality, and volatility analysis in financial markets.

Sara KohtzView profile

Assistant Professor

• Data Science
• Physics-Informed Machine Learning
• Reliability Engineering
• +3 more

Sara Kohtz is an Assistant Professor in the School of Systems Science and Industrial Engineering at Binghamton University. She holds a BS (2016) and MS (2017) in Industrial Engineering from Binghamton University and a PhD in Industrial Engineering from the University of Illinois at Urbana-Champaign (2024). Her research focuses on machine learning theory and applications in high-impact engineered systems, particularly physics-informed machine learning for energy systems, reliability engineering, and fault diagnosis in electrified systems. Education Background: PhD: Industrial Engineering – University of Illinois at Urbana-Champaign MS: Industrial Engineering – Binghamton University BS: Industrial Engineering – Binghamton University Her work bridges data science and engineering, addressing challenges in battery management, sensor placement, and optimal system design. Recent publications emphasize machine learning methodologies for prognostics, energy systems optimization, and physics-driven models. Professional memberships include ASME, IISE, and the Society of Women Engineers (SWE). No awards or grants are explicitly listed in the provided information.

• Autonomous Robot Systems
• Applied Ocean Science
• Quantum Engineering
• +4 more

Mohammad Javad Khojasteh is a Gleason Endowed Assistant Professor in the Department of Electrical and Microelectronic Engineering at the Kate Gleason College of Engineering, Rochester Institute of Technology (RIT). He directs the Khojasteh Autonomous Systems Laboratory (KAS Lab) and is affiliated with RIT's Global Cybersecurity Institute (GCI) and Center for Human-aware AI (CHAI). Before joining RIT, he held postdoctoral positions at Scripps Institution of Oceanography (MPL), MIT’s Department of Mechanical Engineering and LIDS, and Caltech’s CAST, collaborating with NASA JPL’s Team CoSTAR. Education: B.Sc. in Electrical Engineering and Mathematics, Sharif University of Technology (2015) M.Sc. in Electrical and Computer Engineering, UC San Diego (2017) Ph.D. in Electrical and Computer Engineering (Intelligent Systems, Robotics, and Control), UC San Diego (2019) Research Interests: Dr. Khojasteh focuses on autonomous systems, applying machine learning and control theory to enhance robotics, marine autonomous systems, and quantum engineering. His work spans cybersecurity in cyber-physical systems, data-driven optimization, and statistical signal processing. He emphasizes practical applications in ocean science and quantum technologies. Key Contributions: His research has been recognized with awards such as the Tammy L. Blair Student Paper Award. He explores topics like particle flow filters, event-triggered control, and secure autonomous navigation. His lab’s projects include developing robust algorithms for underwater robotics and quantum sensor networks. Advising & Grants: While specific grants are not detailed, his work indicates a focus on multi-agent systems, safety-critical control, and adversarial learning. He advises students in robotics and control systems, though no named advisees are listed. Labs & Teams: Director of KAS Lab, collaborating with GCI and CHAI on human-AI interaction and cybersecurity challenges in autonomous systems.

Justin KinneyView profile

Associate Professor

• Biophysics
• Machine Learning
• Sequence-Function Relationships
• +1 more

Justin Kinney is an Associate Professor at Cold Spring Harbor Laboratory and Interim Chair of the Simons Center for Quantitative Biology. His research combines mathematical theory, machine learning, and experiments to study gene regulation mechanisms. Research Focus: The Kinney Lab develops DNA sequencing tools to dissect biophysical mechanisms of gene regulation, with applications in bacterial transcriptional regulation and alternative mRNA splicing in humans. This work has implications for understanding diseases like Spinal Muscular Atrophy and Cancer. Key Methods: Massively Parallel Reporter Assays (MPRAs), information theory approaches, and machine learning algorithms for analyzing multiplex assays of variant effect (MAVEs). Education: Ph.D. in Physics from Princeton University B.A. in Physics and Mathematics from Cornell University Publication Trends: Recent work focuses on developing interpretable machine learning models for genomic data, biophysical modeling of sequence-function relationships, and mechanistic studies of splice-modifying drugs. Articles demonstrate strong cross-disciplinary integration of physics, computational biology, and molecular genetics.

Mokhtar Z. AlayaView profile

Assistant Professor

• Statistical Machine Learning
• Optimal Transport
• High-dimensional Statistics
• +5 more

Mokhtar Z. Alaya is an Assistant Professor of Statistics and Machine Learning at the Computer Science Department of Université de Technologie de Compiègne (UTC) , where he is affiliated with the LMAC Laboratory . He holds a PhD in Statistical Machine Learning from Université Pierre et Marie Curie (2016) and has conducted postdoctoral research at institutions including Université de Rouen and Université Paris Nanterre. Education: PhD in Statistical Machine Learning, Université Pierre et Marie Curie (2016) Master’s in Statistics and Probabilities, Université Pierre et Marie Curie (2011–2012) Magisterium of Mathematics, University of Gabes, Tunisia (2010) Research Interests: His work focuses on statistical machine learning , including optimal transport for ML applications, high-dimensional statistics, matrix completion, and deep learning. Key projects include interdisciplinary collaborations in mechanics (AI4Meca) and chemistry (AI4Chem), leveraging deep learning for anomaly detection and materials science. Teaching: Courses include Machine Learning , Linear Algebra , and Probability at UTC and international partners like UTSEUS (China). He emphasizes bridging theory and practical coding in Python. Advising & Labs: Supervises PhD students in topics like robust anomaly detection and Frechét regression. Collaborates extensively with the LMAC Lab and cross-disciplinary teams on projects such as generative deep learning for materials engineering.

• Asset Pricing
• Derivatives Pricing
• Term Structure Models
• +3 more

Dr. Nikolaos Karouzakis is an Assistant Professor of Finance at the University of Sussex Business School's Department of Accounting and Finance since 2015. His research focuses on asset pricing, derivatives, term structure models, and Bayesian inference, with publications in leading journals like Management Science and Annals of Tourism Research . Education: PhD in Finance, Bayes Business School (City, University of London) MSc Financial Engineering, Birkbeck College (University of London) MSc Control Systems, Imperial College London BEng Electrical and Electronic Engineering, City, University of London His recent work explores applications of machine learning in financial markets, dynamic term structure modeling, and risk premia analysis. Collaborations include EPSRC-funded research at LSE and consulting with FinTech firms. Teaching: Courses include Financial Instruments, Quantitative Finance, and Applied Research Methods. He maintains active academic-industry connections through consulting roles. Grants & Funding: Previously held a postdoctoral EPSRC grant exploring Bayesian methods in financial econometrics.

Rocío Melissa RiveraView profile

Professor

• Reproductive Physiology
• Epigenetics
• Assisted Reproductive Technologies
• +3 more

Rocío Melissa Rivera is a Professor of Reproductive Physiology and Epigenetics in the Division of Animal Sciences at the University of Missouri. She received her Ph.D. from the University of Florida and completed postdoctoral training at the University of Pennsylvania. Her research investigates epigenetic disruptions in gametes and embryos caused by assisted reproductive technologies (ART). Key projects characterize Large Offspring Syndrome in ruminants and Beckwith-Wiedemann Syndrome in humans—both loss-of-imprinting overgrowth conditions linked to ART. She explores how superovulation and maternal aging alter oocyte DNA methylation patterns and gene expression. Dr. Rivera employs bovine and murine models to identify molecular triggers of epigenetic syndromes, using transcriptomic and chromatin analysis. Current work examines dietary interventions to mitigate ART-associated epigenetic errors. She mentors doctoral students in projects ranging from IGF2R regulation in fetal overgrowth to retroelement control in oocytes. As a Fulbright Senior Scholar at Spain's University of Murcia, she contributed to reproductive biology education. Her laboratory combines developmental biology, epigenetics, and molecular analysis to improve ART safety.

• Ocean Acoustics
• Signal Processing
• Underwater Communications
• +2 more

Zoi-Heleni Michalopoulou is Professor in Mathematical Sciences at New Jersey Institute of Technology, specializing in ocean acoustics and signal processing. Her research develops advanced algorithms for underwater signal detection, geoacoustic inversion, and ocean parameter estimation. Current projects include Navy-sponsored investigations into shallow water inversion using machine learning and optimization methods. Research integrates Gaussian processes, genetic algorithms, and Bayesian methods for solving inverse problems in uncertain ocean environments. Recent publications focus on multichannel signal detection in distorted underwater channels, direction-of-arrival estimation, and particle filter optimization. Maintains active collaborations with Navy research divisions. Publications demonstrate consistent methodological innovation in statistical signal processing, with applications to underwater sensor networks and acoustic characterization. Currently leads NSF-funded nanotechnology education initiatives.

• Robust AI
• Algorithmic Decision-Making
• Reinforcement Learning
• +3 more

Ilija Bogunovic is an Assistant Professor in the Department of Electronic and Electrical Engineering at University College London (UCL). His research focuses on robust AI, algorithmic decision-making, and reinforcement learning, with applications to large language models, safe RL, and human-AI interaction. He leads a team of PhD students and collaborates with institutions like UKAEA and Google DeepMind. His work has been recognized through the Google Research Scholar Program Award and EPSRC New Investigator Award for robust decision-making and RL. Research interests include non-stationary preference optimization, adversarial robustness in decision transformers, and group-robust preference alignment. He advises over a dozen PhD students and actively contributes to conferences like NeurIPS, ICML, and AISTATS. His methods address challenges in distribution shifts, class imbalance, and safe multi-agent systems. Key achievements include developing algorithms like REDUCR for robust data downsampling and AE-LSVI for active reinforcement learning. He organizes reading groups on adaptive experimental design and co-organized NeurIPS workshops on drug discovery and AI alignment.

Praveen KumarView profile

Professor

• Hydrology
• Environmental Engineering
• Sustainability Science
• +4 more

Praveen Kumar is a distinguished academic and researcher in civil and environmental engineering at the University of Illinois. He holds the Colonel Harry F. and Frankie M. Lovell Endowed Professorship and serves as Professor in the Department of Civil and Environmental Engineering, along with multiple affiliations including the Prairie Research Institute and National Center for Supercomputing Applications (NCSA). His career spans roles from Assistant Professor (1995-2001) to his current senior academic positions. Education: B.Tech. (IIT Bombay, 1987), M.S. (Iowa State, 1989), Ph.D. (Minnesota, 1993), all in civil engineering. His research focuses on Hydrocomplexity —quantifying interactions between soil, water, climate, vegetation, and human systems. He leads NSF-funded projects like EarthCube and SEAD, and directs the Critical Zone Observatory for Intensively Managed Landscapes. Research interests include cyber-infrastructure for data models, climate change impacts, and sustainable water solutions. Notable awards include the NASA New Young Investigator Award (1996) and Xerox Faculty Research Award (2005). He has served as Editor-in-Chief of Water Resources Research and held editorial roles in multiple journals. Teaching excellence recognized via multiple listings in UIUC’s Teachers Rated as Excellent. Academic service includes roles on campus sustainability committees, NSF grant panels, and AGU committees. He advises students in hydrology and sustainability, with notable advisees like Patricia Saco and Francina Dominguez.

Giulia De PasqualeView profile

Assistant Professor

• Control Systems
• Social Network Analysis
• Optimization Algorithms
• +3 more

Giulia De Pasquale is an Assistant Professor at the Department of Electrical Engineering, Eindhoven University of Technology, specializing in Control Systems. Her research focuses on social network interventions, optimization algorithms, and fairness in AI systems. She leads the Control Systems Group, addressing challenges in distributed control, hypergraph reconstruction, and ethical algorithm design. Her work integrates machine learning, network science, and control theory to develop fair and safe recommendation systems. Recent projects include hypergradient-based optimization for social network interventions and optimal transport approaches to fairness in influence maximization. She actively collaborates with institutions like Santa Fe Institute and UC San Diego. Giulia organizes interdisciplinary workshops on algorithmic fairness and has presented at NeurIPS, CDC, and NetSci. She advises PhD students like Alessandro Casu and hosts visiting scholars such as Camilla Quaresmini. Her research has been published in prestigious venues including Nature Communications and ICML.

Hans-Georg MüllerView profile

Professor

• Functional Data Analysis
• Fréchet Analysis
• Longitudinal Studies
• +3 more

Hans-Georg Müller is a Distinguished Professor of Statistics at the University of California, Davis. His work focuses on functional data analysis, Fréchet analysis of random objects, and applications in longitudinal studies, brain imaging, and aging research. He has contributed to foundational methodologies such as Fréchet regression and Wasserstein-based statistical models. Key research interests include the statistical analysis of non-Euclidean data, covariance structures in high-dimensional spaces, and temporal dynamics of complex systems. Müller has published extensively in top-tier journals like the Annals of Statistics and IEEE Transactions on Information Theory. Awards: 2022 Rietz Lecture & Award (IMS), 2017 Humboldt Research Award, and multiple fellowships from prestigious societies. Advising: Recognized with the 2020 Graduate Program Advising & Mentoring Award at UC Davis. Publications: Over 200 papers, including influential works on Fréchet regression, Cox process regression, and longitudinal brain development studies. His research bridges theoretical statistics with applied domains, particularly in understanding neurodevelopmental processes and the statistical mechanics of aging biomarkers.

Debashis PaulView profile

Professor

• Multivariate statistics
• Random matrix theory
• Functional data analysis
• +4 more

Debashis Paul is a Professor in the Department of Statistics at the University of California, Davis. His research focuses on high-dimensional statistics, random matrix theory, functional data analysis, and their applications in neuroimaging and spatial statistics. He has contributed to methodologies for spectral analysis, covariance modeling, and nonparametric estimation in complex datasets. His work spans theoretical developments in multivariate analysis and practical applications in fields such as medical imaging and genomics. Recent projects include modeling fiber orientation in diffusion MRI, analyzing high-dimensional genomic data, and studying pandemic dynamics through statistical frameworks. Key research themes include: High-dimensional time series analysis Random matrix theory applications Functional data smoothing techniques Non-Gaussian spatial field modeling Covariance structure inference His recent publications highlight advancements in spectral estimation, latent graph inference, and meta-learning frameworks in high-dimensional settings. He has also contributed to methodological improvements in GWAS analysis and nonautonomous dynamical systems modeling.