Find Academic Supervisors

• Computer Vision
• Deep Learning
• Algorithmic Fairness
• +4 more

Dr. Viktoriia Sharmanska is an Assistant Professor in Artificial Intelligence at the University of Sussex, UK, and an honorary lecturer at Imperial College London. She leads research in computer vision and machine learning, focusing on algorithmic fairness, human behavior analysis, and generative models. Her work bridges theoretical advancements with practical applications in healthcare, urban development, and ethical AI. Education: PhD in Computer Vision and Machine Learning, Institute of Science and Technology Austria (2015) MSc in Applied Mathematics (summa cum laude), Taras Shevchenko National University of Kyiv, Ukraine (2009) Research Interests: Her interdisciplinary research spans algorithmic fairness, 3D vision, and multi-modal toxicity moderation. She develops methods to address bias in AI systems and innovate in facial behavior analysis and video synthesis. Notable contributions include frameworks for fair representation learning and the DAD-3DHeads dataset for 3D head alignment. Articles Overview: Her recent work emphasizes generalization techniques (e.g., Okapi), statistical matching for fairness, and generative models like Free-HeadGAN. These contributions highlight advancements in both technical rigor and societal impact. Awards & Grants: Imperial College Research Fellowship (2017–2020) CVPR2019 Outstanding Reviewer Award Lead researcher on EPSRC-funded 'Generative Video Models for Visual Speech Synthesis' (2018–2021) Advising & Collaboration: Collaborates with UCU/Pinata Farm and CTU Prague on 3D facial modeling. Organizes initiatives like Women in Computer Vision (WiCV2018) and serves as Area Chair for ICLR2019–2023 and CVPR2021. Labs & Teams: Member of the Predictive Analytics Lab (PAL) at Sussex and part of the ELLIS network for European AI research. Active in cross-disciplinary projects with medical and environmental scientists.

Micah GoldblumView profile

Assistant Professor

• Machine Learning
• AI Safety
• Automated Data Science
• +5 more

Micah Goldblum is an Assistant Professor at Columbia University, focusing on machine learning research with emphasis on AI safety, automated data science, and large-scale model training strategies. His work explores foundational topics like generalization theory, Bayesian inference, and algorithmic reasoning. He received a Ph.D. in Mathematics from the University of Maryland, advised by Tom Goldstein and Wojciech Czaja, followed by a postdoctoral fellowship at New York University under Yann LeCun and Andrew Gordon Wilson. His research portfolio includes developing robust benchmarks for large language models (e.g., LiveBench), analyzing inductive biases in ML systems via Kolmogorov complexity, and advancing adversarial detection techniques like Binoculars. Key contributions span LLM evaluation, diffusion model theory, and fairness in computer vision systems. Goldblum’s work on Bayesian model selection and compression bounds for large language models has provided critical theoretical insights. Goldblum has been recognized with the 2022 ICML Outstanding Paper Award for his work on Bayesian model selection. His research also addresses societal challenges, such as protecting privacy in facial recognition systems (LowKey) and mitigating biases in neural architectures. Collaborations include leading projects like the Battle of the Backbones vision benchmark and exploring adversarial robustness in federated learning. His labs and teams focus on interdisciplinary applications of ML, including automated benchmarking, secure AI deployment, and ethical alignment of LLMs. Goldblum’s publications consistently bridge theory and practice, with recent breakthroughs in diffusion model design, continual learning algorithms, and understanding the intrinsic properties of data distributions.

James HopgoodView profile

Professor

• Statistical Signal Processing
• Machine Learning
• Data Science
• +5 more

James Hopgood is a Professor of Statistical Signal Processing at the University of Edinburgh's School of Engineering, leading the Institute for Data, Imaging, and Communications. He holds roles as Dean of Quality and Enhancement for the College of Science and Engineering and Director of the EPSRC/MoD CDT in Sensing, Processing & AI for Defence and Security (SPADS). His research focuses on statistical signal processing applications in medical imaging, acoustic sensing, and multi-target tracking. Hopgood earned his M.A., M.Eng., and Ph.D. from the University of Cambridge, joining Edinburgh in 2004. He has developed algorithms for Bayesian signal processing, acoustic source localization, and medical imaging, with over 100 publications. As Editor-in-Chief of the IET Journal of Signal Processing since 2011, he contributes to academic leadership. Teaching includes courses on signal processing, probability, and sensor networks, emphasizing visualization and real-world applications. Current projects span adaptive optical imaging for medical diagnostics and probabilistic graphical models for multi-target tracking. His work bridges academia and industry, collaborating with companies like Leonardo and Agilent Technologies. He supervises numerous PhD students and leads multidisciplinary initiatives in defense, healthcare, and autonomous systems. **Education**: PhD in Statistical Signal Processing, University of Cambridge (2001) MEng in Electrical and Information Sciences, University of Cambridge (1997) MA, University of Cambridge (2000) **Research Interests**: His work spans model-based Bayesian methods, acoustic source localization, medical imaging (e.g., fluorescence lifetime imaging), and multi-target tracking. Recent projects include optimizing UAV trajectories in dynamic environments and developing algorithms for distributed sensor networks. **Grants & Collaborations**: Funded by EPSRC/MoD, GSK, and Leonardo, his projects address defense sensing, drug development, and clinical imaging. Collaborations include Firefinch (software/data science) and the UDRC Summer School. **Labs/Teams**: Leads the Acoustics and Audio Group and the SPADS CDT, fostering innovation in sensing and AI for defense/security applications.

Thomas CourtadeView profile

Associate Professor

• Information Theory
• Data Science
• Network Communication
• +5 more

Thomas Courtade is an Associate Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He joined Berkeley in 2014 after a postdoctoral fellowship at Stanford University, supported by the NSF Center for Science of Information. His research focuses on information theory, data science, and their intersections with machine learning and privacy-preserving algorithms. Education: Ph.D. in Electrical Engineering, University of California, Los Angeles (2012) M.Sc. in Electrical Engineering, University of California, Los Angeles (2008) B.Sc. in Electrical Engineering, Michigan Technological University (2007, summa cum laude) Research Interests: Information Theory and its applications to network communication Privacy-preserving data analysis and differential privacy Statistical estimation under heterogeneous privacy constraints Optimization in distributed systems and market design Functional inequalities (Brascamp-Lieb, Poincaré-Korn) Machine learning with emphasis on model robustness and efficiency Awards and Fellowships: Electrical Engineering Award for Outstanding Teaching (2020) Hellman Fellow (2016) Advising and Grants: Supervised no listed students (student names not provided in text) Recipient of NSF CAREER Award (2018) Labs and Collaborations: Berkeley Laboratory for Information and System Sciences (BLISS) Center for Theoretical Foundations of Learning, Inference, and Mathematics (CLIMB)

• Empirical Industrial Organization
• Quantitative Marketing
• Advertising Economics
• +4 more

Bradley Shapiro is a Professor of Marketing and True North Faculty Scholar at the University of Chicago Booth School of Business. His research spans empirical industrial organization, quantitative marketing, and the economics of advertising, with applications in health insurance, pharmaceuticals, digital advertising, and firearms regulation. He teaches MBA Marketing Strategy, Executive MBA Marketing Management, and PhD Health Economics seminars. Education includes a Ph.D. in Economics from MIT, an M.S. in Mathematics, a B.S. in Mathematics, and a B.A. in Economics from Virginia Tech. His research investigates advertising effectiveness, consumer behavior under regulatory frameworks, and societal impacts of marketing. Key areas include measuring digital advertising value, firearm demand dynamics, depression's influence on shopping patterns, and pharmaceutical advertising outcomes. Methodologically, he employs large-scale experiments, causal inference techniques, and cross-category analyses to derive generalizable insights. Publications emphasize advertising ROI, regulatory policy simulations, and behavioral economics. Recent work analyzes offsite data value for Meta advertisers, firearm regulation impacts, and TV advertising elasticity distributions across industries. Articles consistently demonstrate interdisciplinary integration of marketing, economics, and public policy. Awards: True North Faculty Scholar

Daniel OiView profile

Senior Lecturer

• Quantum Information
• Quantum Computation
• Quantum Optics
• +7 more

Daniel Oi is a Reader in the Computational Nonlinear and Quantum Optics (CNQO) group at the Department of Physics, University of Strathclyde, under the Faculty of Science. He is also affiliated with SUPA (Scottish Universities Physics Alliance), Ocean, Air and Space, and StrathCyber. He holds an honorary Senior Lecturer position at the University of Bristol's Merchant Venturers School of Engineering (2020–2023). He is actively involved in quantum research and is accepting PhD students. BSc Hons Physics, University of Western Australia (1995) BEng Hons Mechanical Engineering, University of Western Australia (1997) MASt (Part III Mathematics), University of Cambridge (1999) PhD in Quantum Channels, Mixed States, and Interferometry, University of Oxford (2002) Daniel Oi's research spans fundamental quantum theory, quantum engineering, quantum computation, and quantum space technologies. His work emphasizes quantum communication, quantum optics, and quantum information, with applications in space-based quantum key distribution and satellite quantum networks. He explores quantum retrodiction, entanglement, and quantum metrology, bridging theoretical models with experimental implementations. His research contributes to UN Sustainable Development Goals, particularly in education and technological innovation. His recent publications (2020–2025) show a strong focus on space quantum communication, including satellite QKD, quantum repeaters, atom interferometry, and radiation-hardened photodetectors. His work combines theoretical advances with practical engineering for space deployment, emphasizing performance under finite resources and real-world constraints. Key themes include quantum networking, entanglement distribution, and quantum-enhanced sensing using interferometric techniques. He received the recognition of being a founding member of QUISCO (Quantum Information Scotland Network) in 2008. Daniel Oi has been a principal or co-investigator on multiple research projects, including EPSRC-funded CASE accounts, the DTP 2224 studentship, NPL iCASE on quantum transduction, the Integrated Quantum Networks Hub, and ESA’s VOLT Mission. He has hosted academic visitors, organized workshops, and delivered invited talks globally. He supervises research students and contributes to datasets in quantum scheduling and state amplification. He is actively involved in professional activities, including organizing the INSQT Workshop 5, speaking at the Satellite Quantum Key Distribution event, and participating in quantum networks and gravity workshops. His lab, the CNQO group, focuses on computational and theoretical aspects of nonlinear and quantum optics, supporting satellite quantum technologies and foundational quantum research.

Nitis MukhopadhyayView profile

Professor

• Sequential Analysis
• Statistical Inference
• Confidence Interval Estimation
• +5 more

Nitis Mukhopadhyay is a Professor in the Department of Statistics at the University of Connecticut, with extensive research contributions in sequential analysis and statistical inference. His academic work focuses on developing innovative methodologies for confidence interval and point estimation problems, particularly in sequential and multistage sampling frameworks. His research has significant applications across various domains including environmental science, clinical trials, and survey methodology. Professor Mukhopadhyay's research interests center on sequential analysis, with particular emphasis on confidence interval estimation, point estimation, survey sampling, environmental sampling, clinical trials, and multivariate data analysis. His work bridges theoretical statistical developments with practical applications, developing methodologies that address real-world data challenges while maintaining rigorous mathematical foundations. His research often involves complex statistical problems requiring sophisticated solutions that balance accuracy with efficiency. Analysis of Professor Mukhopadhyay's recent publications reveals a strong focus on advanced sequential methodologies, particularly in minimum risk point estimation, fixed-width confidence interval problems, and multistage sampling strategies. His work demonstrates increasing attention to big data contexts and computational implementations, while maintaining rigorous theoretical foundations. The publications show consistent development of second-order asymptotic properties and practical implementations across various parametric families including normal, exponential, and gamma distributions. Professor Mukhopadhyay maintains active correspondence with the statistical community through his office at AUST 331 on the Storrs Campus of the University of Connecticut. His work continues to influence methodological developments in sequential analysis and statistical inference, with applications spanning environmental monitoring, clinical research, and various scientific domains requiring sophisticated sampling strategies.

• Neural Computation
• Artificial Intelligence
• Machine Learning
• +10 more

Christopher L. Buckley is Professor of Neural Computation (Informatics) at the School of Engineering and Informatics, University of Sussex, where he has been a faculty member since 2014. He was promoted from Lecturer to Senior Lecturer in 2018 and to Professor in 2022. His research bridges theoretical neuroscience and artificial intelligence, with a focus on active inference, predictive coding, and embodied cognition. Master of Physics (First Class), University of Edinburgh, 2000 MSc in Evolutionary and Adaptive Systems, University of Sussex, 2001 PhD in Cognitive Robotics, University of Southampton, 2008 His research interests center on understanding how neural systems give rise to robust behavior and how such principles can inform AI. He investigates machine learning models grounded in neuroscience, particularly through the lens of the free energy principle and active inference. His work spans artificial intelligence, machine learning, theoretical neuroscience, cognitive robotics, and artificial life , with applications in planning, perception, and adaptive systems. His recent publications reveal a strong trend in scaling predictive coding, hybrid active inference models, and rate-distortion theory for action representation. He frequently collaborates with leading researchers like Karl Friston and explores topics such as tool use, synthetic awareness, and ecosystem-level intelligence. His work appears in high-impact journals and preprint servers including Neural Computation , Frontiers in Network Physiology , and arXiv. He has received multiple research grants from prestigious funders including Innovate UK, the John Templeton Foundation, BBSRC, and the European Union, supporting projects on synthetic awareness, neural abstraction, and brain-wide dynamics in vertebrates. A metapredictive model of synthetic awareness for enabling tool invention (Innovate UK) Modelling Abstractions in Deep Reinforcement Learning and Rate-Distortion Theory (VERSES INC) The Scaling-up of Purpose in Evolution (John Templeton Foundation) DIMENSIVE: Data-driven Inference of Models from Embodied Neural Systems (EU) Distributed neural processing of self-generated visual input (BBSRC) Christopher Buckley leads an active research group at Sussex, contributing to the development of next-generation AI systems grounded in biological principles. He has no listed students in the provided data, but his collaborative network is extensive. He is not part-time, not retired, and not a former staff member, indicating ongoing active engagement in research and teaching.

Bartosz KołodziejekView profile

Associate Professor

• Probability Theory
• Statistics
• Stochastic Fixed Point Equations
• +7 more

Bartosz Kołodziejek is an Associate Professor at the Faculty of Mathematics and Information Science, Warsaw University of Technology. His research lies at the intersection of probability theory and mathematical statistics, with a focus on stochastic fixed point equations, free probability, and graphical models. He actively publishes in top-tier journals and maintains a strong collaborative network. His research interests include: Stochastic fixed point equations and their applications Characterizations of probability distributions on symmetric and homogeneous cones Free probability and free convolution Graphical models and high-dimensional statistics Symmetry in Gaussian models and model selection Perpetuities and tail behavior of stochastic recursions The analysis of his recent publications reveals a consistent trend in theoretical probability and statistical methodology, particularly in high-dimensional inference, random matrix theory, and invariant models. His work often involves deep analytical techniques and connections across fields such as convex analysis, exponential families, and stochastic processes. He has no listed scientific awards on his homepage. While no students or grants are explicitly mentioned, his active publication record and software contributions (such as the gips R package) suggest ongoing research leadership. He collaborates with prominent researchers in probability and statistics across Europe. He is involved in the development of statistical software, notably contributing to the theoretical foundations of the gips package for Gaussian models with permutation symmetry, used in high-dimensional data analysis.

• Probability Theory
• Mathematical Statistics
• Characterizations of Distributions
• +7 more

Prof. Dr. hab. Jacek Wesołowski is a faculty member at Warsaw University of Technology, affiliated with the Faculty of Mathematics and Information Science, where he serves as Head of the Department of Probability and Mathematical Statistics. He is actively involved in research and teaching, with a focus on probability theory and mathematical statistics. His research interests include probability theory, mathematical statistics, characterizations of distributions, stochastic processes, survey sampling, multivariate analysis, free probability, and quadratic harnesses . These areas are evident from his extensive publication record in top-tier journals such as the Annals of Probability, Journal of Multivariate Analysis, and Stochastic Processes and their Applications. The trends in his recent publications reveal a strong focus on distributional characterizations (e.g., gamma, Kummer, Wishart), regression properties of order and record statistics , quadratic harnesses and martingale structures , and methodological contributions to survey sampling and imputation . His work often bridges theoretical probability with statistical applications. No scientific awards were mentioned in the provided text. He advises graduate students, as indicated by his organization of probabilistic workshops for PhD and master’s students. He has collaborated extensively with researchers such as W. Bryc, G. Letac, H. Massam, and G. Rempała. While no specific grants are listed, his sustained research output suggests active funding. He teaches core courses such as Probability Calculus I/II and Survey Sampling. There is no mention of specific labs or research teams, but his departmental leadership and collaborative publications suggest an active research group environment.

Anton SchickView profile

Professor

• Large Sample Theory
• Semiparametric Statistics
• Nonparametric Models
• +8 more

Anton Schick is a Bartle Professor in the Department of Mathematics and Statistics at Binghamton University. His research focuses on advanced statistical methodologies, including large sample theory, semiparametric and nonparametric models, and efficient inference for complex data structures like Markov chains and stochastic processes. Education: Ph.D. from Michigan State University His work addresses statistical challenges in handling incomplete data, curve estimation, and U-statistics. Recent publications emphasize empirical likelihood methods for Markov chains, density estimation for linear and nonlinear processes, and robust regression techniques under missing data scenarios. The articles highlight his expertise in time series analysis, nonparametric regression, and statistical inference. Key themes include efficiency of estimators, convergence properties of density estimators, and applications of empirical likelihood to martingales and autoregressive models.

Gregor BöhlView profile

Researcher

• Dynamic Macroeconomics
• Macroeconometrics
• Econometrics
• +12 more

Dr. Gregor Böhl is a Principal Investigator at the Institute for Macroeconomics and Econometrics within the Department of Economics at the University of Bonn. His research is fully funded by the German Research Foundation (DFG) under project 441540692, focusing on dynamic macroeconomics, econometrics, and public economics. He is also affiliated with the CRC TR 224 EPoS and contributes to the OSE initiative for teaching and research infrastructure. His research interests lie at the intersection of Dynamic Macroeconomics , Heterogeneous Agent Models (HANK) , DSGE modeling , and computational economics . He investigates how household heterogeneity shapes macroeconomic outcomes, particularly in the context of inequality , climate policy , monetary financing , and financial frictions . His methodological expertise includes nonlinear solution techniques, Bayesian estimation under occasionally binding constraints, and high-performance computational tools. The recent trend in his publications reveals a strong focus on the empirical and structural analysis of monetary policy , especially at the zero lower bound, the macroeconomic implications of climate policies , and the development of robust computational methods for solving complex economic models. His work combines theoretical modeling with advanced numerical techniques and open-source software development. 2017 Student Prize of the Society for Computational Economics Dr. Böhl has secured competitive research funding from the DFG and the OSE initiative. He actively mentors researchers and collaborates with leading economists such as Cars Hommes, Felix Strobel, and Gavin Goy. His academic contributions extend beyond publications to the development of widely used open-source software packages like pydsge , econpizza , and dime_sampler , which support estimation, simulation, and inference in macroeconomic models. He previously held a postdoctoral position at IMFS, Goethe University Frankfurt, with collaborations at the Hoover Institution, Stanford University. He leads a research group focused on computational macroeconomics and maintains a strong commitment to open science, reproducibility, and the use of free and open-source software (FOSS) in economic research. His team develops and maintains several GitHub repositories that provide tools for nonlinear filtering, Bayesian inference, and HANK model solutions.

Dan MikulincerView profile

Assistant Professor

• High-Dimensional Geometry
• Probability Theory
• Statistics
• +10 more

Dan Mikulincer is the Brian and Tiffinie Pang Assistant Professor at the University of Washington in the Department of Mathematics, College of Arts and Sciences. He previously held a postdoctoral Instructor position at MIT Mathematics and earned his Ph.D. from the Weizmann Institute of Science under Ronen Eldan. He completed his B.Sc. in Mathematics and Computer Science at Ben-Gurion University, where he also studied Cognitive Neuroscience. B.Sc.: Ben-Gurion University (Mathematics, Computer Science, Cognitive Neuroscience) Ph.D.: Weizmann Institute of Science, Faculty of Mathematics Postdoc: MIT Mathematics Current: Assistant Professor, University of Washington, Department of Mathematics His research lies at the intersection of high-dimensional geometry, probability, statistics, information theory, and data science. He is particularly focused on normal approximations, Stein's method, stochastic analysis, and dimension-free phenomena. His work explores foundational aspects of learning theory, random matrices, transportation inequalities, and neural networks, often using probabilistic and analytic tools to derive sharp, robust results in high dimensions. The recent publications reflect a consistent focus on probabilistic methods in high-dimensional settings. Key themes include normal approximation via Stein's method, optimal transport, concentration and anti-concentration inequalities, random graph models, and theoretical aspects of machine learning such as learnability and neural network expressivity. The work spans both pure mathematics (e.g., GAFA, PTRF) and top-tier computer science venues (e.g., COLT, STOC, NeurIPS), highlighting interdisciplinary impact. Although no formal scientific awards are listed in the provided text, his publications in premier journals and conferences (Annals of Probability, STOC, NeurIPS, COLT) indicate significant recognition in the theoretical community. Dan Mikulincer has advised or collaborated with several researchers including Yair Shenfeld, Max Fathi, Ronen Eldan, and Sébastien Bubeck. He has served as a TA for 18.650: Statistics for Applications at MIT and taught programming courses (Java, Python, JavaScript) at the Interdisciplinary Center Herzliya. He is also a senior lecturer at WeCode, a nonprofit providing free programming education to underrepresented youth in Israel, indicating a strong commitment to education and outreach. He has been affiliated with research groups at MIT Mathematics, Weizmann Institute, and Microsoft Research AI, where he spent the summer of 2019 hosted by Sébastien Bubeck. These collaborations span theoretical machine learning, stochastic processes, and algorithmic foundations.

Behnam SobhaniaraghView profile

Assistant Professor

• Computational Mechanics
• Solid Mechanics
• Fracture Mechanics
• +12 more

Dr Behnam Sobhaniaragh is a Lecturer (Assistant Professor) in Mechanical Engineering at Teesside University, affiliated with the School of Computing, Engineering and Digital Technologies. He holds a PhD from Ghent University and a DSc from UFRJ, Brazil, and is a Chartered Engineer (CEng) with IMechE and a Fellow of the Higher Education Academy (FHEA). PhD in Electro-Mechanical Engineering, Ghent University, Belgium Doctorate of Science (DSc) in Computational Mechanics, Federal University of Rio de Janeiro (UFRJ), Brazil Bachelor’s and Master’s degrees inferred from academic trajectory in Mechanical/Electro-Mechanical Engineering His research focuses on computational mechanics of lightweight and sustainable materials, with expertise in fracture mechanics , phase-field modeling , hydrogen embrittlement , and multifunctional materials . He investigates material behavior under multiphysics conditions, contributing to Net Zero goals through advanced modeling of structural integrity and degradation. His work bridges solid mechanics, materials science, and physics, emphasizing durability and efficiency. The analysis of his recent publications reveals a strong trend in phase-field modeling of fracture , functionally graded and nanocomposite materials , thermo-mechanical coupling , and sustainable material systems . His research integrates finite element methods, micromechanical modeling, and data-driven approaches to simulate crack propagation, stability, and performance in advanced engineering materials, particularly under extreme or coupled environmental conditions. His scientific recognition includes: Fellow of the Higher Education Academy (FHEA) Chartered Engineer (CEng), Institution of Mechanical Engineers (IMechE) Recipient of the prestigious FAPESP postdoctoral fellowship in Brazil Best DSc Thesis Award in the Department of Civil Engineering, UFRJ Dr Sobhaniaragh actively supervises MSc and BEng final year projects and is accepting PhD students. He leads the research project Microstructural Integrity of Shape Memory Alloys used in Elastocaloric Refrigeration , funded by the Newton Fund. He serves as a guest editor for special issues on Programmable Metamaterials and Phase-Field Modeling , and is a reviewer for over 20 international journals including Computer Methods in Applied Mechanics and Engineering and Engineering Fracture Mechanics . He contributes to academic leadership as Module Leader for Machine Design , Continuum Mechanics , and Aircraft Structures , and as tutor for several core engineering modules. His research group focuses on computational modeling of material degradation and smart structures, collaborating internationally with institutions in Germany, Brazil, and the UK.

Scott C. SchmidlerView profile

Associate Professor

• Computational Biology
• Bioinformatics
• Statistical Science
• +12 more

Scott C. Schmidler is an Associate Professor in the Department of Statistical Science and Computer Science at Duke University, affiliated with the Duke Center for Systems Biology. His research lies at the intersection of statistics, computer science, and molecular biology, focusing on the development of probabilistic and computational methods for understanding biological systems. Department of Statistical Science, Duke University Department of Computer Science, Duke University Duke Center for Systems Biology Dr. Schmidler's research interests include Monte Carlo simulation, Markov chain convergence, shape analysis, structural biology, and biophysics. He develops statistical models and algorithms to study protein folding, molecular simulation, and systems biology. His work integrates geometric and stochastic methods to model complex biological data, particularly in the context of structural alignment and evolutionary modeling. His recent publications and talks reflect a strong focus on convergence diagnostics in MCMC, adaptive sampling methods in molecular simulations, Bayesian inference, and the statistical challenges in modeling biomolecular systems. The articles span topics from theoretical foundations of Markov chains to practical applications in vaccine design and protein evolution. He has served as Associate Editor for the Annals of Applied Statistics and as Program Coordinator for the Master's program in Statistical Science at Duke. He has co-organized several major workshops, including events at BIRS and SAMSI, indicating active leadership in the statistical and computational biology communities. Co-organizer, BIRS Workshop on Advances in Scalable Bayesian Computation (2014) Co-organizer, SAMSI Workshop on Molecular Motors, Neuron Models, and Epidemics on Networks (2010) Associate Editor, Annals of Applied Statistics (2009–present) Program Coordinator, Master's in Statistical Science Dr. Schmidler advises students and postdoctoral researchers in computational biology and statistical science. While specific names are not listed, his group is involved in cutting-edge research at the interface of statistics and molecular biology. He has secured participation in major collaborative programs such as SAMSI’s Stochastic Dynamics program, reflecting external recognition and funding support for his work. He is involved in several research teams and centers, including the Duke Center for Systems Biology, and has contributed to interdisciplinary collaborations in enhanced sampling methods, Bayesian computation, and network-based biological modeling. His ongoing work continues to advance the statistical foundations of molecular simulation and systems biology.