Find Academic Supervisors

• Theoretical Computer Science
• Graph Algorithms
• Parameterized Algorithms
• +2 more

Shivesh K. Roy is a postdoctoral researcher at the Institute of Mathematical Sciences (IMSc), Chennai, working with Prof. Saket Saurabh. Previously, he completed his Ph.D. at Eindhoven University of Technology under Prof. Bart Jansen, supported by an ERC Starting Grant titled "Rigorous Search Space Reduction". His educational background includes: Ph.D. in Algorithms, TU Eindhoven (2024); Thesis: "Parameterized Algorithms for Augmented Graph Problems" His research focuses on theoretical computer science, specifically graph algorithms, parameterized complexity, and kernelization. He develops rigorous algorithmic frameworks for NP-hard graph problems by exploiting structural properties and parameterization techniques to achieve efficient solutions. His publications (2021-2025) reveal consistent advancement in kernelization methods and parameterized algorithms, particularly for vertex cover, feedback vertex set, and clique packing problems. Key innovations include linear-vertex kernels for sparse graphs and hardness results for weight compression, demonstrating deep integration of combinatorial structures with computational complexity. He has contributed academic service through peer review for SIAM Journal on Discrete Mathematics, Algorithmica, FCT 2025, SOFSEM 2025, and WG 2022. Currently embedded in Prof. Saket Saurabh's research group at IMSc, he continues collaborative work initiated during his ERC-funded doctoral research at TU Eindhoven's Algorithms group.

Russell PoldrackView profile

Professor

• Cognitive Neuroscience
• Neuroinformatics
• Decision-Making
• +5 more

Russell Poldrack is the Albert Ray Lang Professor of Psychology at Stanford University , with an additional courtesy appointment in Psychiatry and Behavioral Sciences. He serves as Department Chair in Psychology, Associate Director of the Stanford Data Science Initiative, and Director of the Center for Open and Reproducible Science. A British Academy Fellow (2023), Poldrack leads research at the intersection of cognitive neuroscience and neuroinformatics , focusing on decision-making, executive control, and open science frameworks like NeuroSynth and OpenNeuro. Developed fMRIPrep pipeline for standardized neuroimaging preprocessing Championed reproducibility in fMRI analysis through multiverse analysis frameworks Created hyve visualization engine for composite neuroimaging representations His recent publications address decision-making mechanisms (eLife 2025), fMRI reliability (Nature Human Behaviour 2024), and machine learning reproducibility (Science Advances 2024). Poldrack's lab has produced over 150 published reuses of shared neuroimaging data through the OpenNeuro platform, while his Poldrack Lab continues to explore self-regulation ontology in clinical populations. He currently oversees 13 independent studies through Stanford's Neuroimaging Data Science program. Major awards: OHBM Open Science Award , Wiley Young Investigator (2005) Professional roles: Board of Scientific Counselors, NIH NIMH; Former Human Connectome Project EAB Chair Methodological contributions: BIDS standard adoption, MRIQC quality control protocols

Melissa K. BeasonView profile

Academic

• Atmospheric Propagation
• Optical Turbulence
• Laser Beam Propagation
• +3 more

Dr. Melissa K. Beason is a Research Assistant Professor of Engineering Physics at the Air Force Institute of Technology (AFIT), specializing in atmospheric propagation and laser beam control. She holds a PhD in Electrical Engineering from the University of Central Florida, an MS in Mathematical Sciences from UCF, and a BS in Physics from the University of Florida. Her research focuses on optical turbulence theory, beam propagation modeling, and experimental methods for characterizing atmospheric conditions. Dr. Beason serves as an Air Force Subject Matter Expert for JDETO Beam Control & Propagation, NATO SCI-316 member, and U.S. representative to NATO SET-304. Her work includes collaborative projects on turbulence profiling, sensor development (e.g., SMASH), and applications of sonic anemometry for turbulence parameter estimation. Education: PhD, Electrical Engineering, University of Central Florida MS, Mathematical Sciences, University of Central Florida BS, Physics, University of Florida Awards: European Research Consortium Fellowship (2019) Preeminent Postdoctoral Scholar (2019) Special Act Award (1996) Her research spans theoretical and experimental studies in atmospheric optics, including turbulence parameterization, beam wander effects, and free-space optical communication metrics. Recent work emphasizes non-Kolmogorov turbulence models, anisotropic effects, and sensor validation for defense applications. She has advised over 20 years of R&D projects involving sonar systems, laser research, and collaborative NATO initiatives. Dr. Beason's contributions include co-authoring textbooks on laser beam propagation and asymptotic methods in wave theory. Labs/Teams: Involved in AFIT's Engineering Physics research group, cross-functional teams at NATO, and partnerships with organizations like the Naval Undersea Warfare Center.

Orlando AyalaView profile

Associate Professor

• Multiphase Flows
• Turbulent Flows
• Particle Transport in Fluid Flows
• +5 more

Dr. Orlando Ayala is an Associate Professor in the Mechanical Engineering Technology Department at Old Dominion University's Frank Batten College of Engineering and Technology. He holds a Ph.D. (2005) and M.Sc. (2001) in Mechanical Engineering from the University of Delaware, and a B.S. in Mechanical Engineering (Cum Laude, 1995) from Universidad de Oriente, Venezuela. His research focuses on multiphase flows , turbulent particle transport , and high-performance computational methods . Key areas include: fluid-particle interactions in pipeline erosion, lattice Boltzmann modeling for turbulence modulation, cloud droplet collision dynamics, and porous media flow. His work integrates advanced numerical simulations with applications in energy systems and environmental fluid mechanics. Publications emphasize turbulent collision statistics, particle-laden flows, and parallel computing algorithms, with recent studies leveraging DNS and lattice Boltzmann methods to resolve microscale interactions in multiphase systems. Scientific Awards & Honors: Certificate of Excellence in Undergraduate Research (2017) Highly Cited Research in Parallel Computing (2016) Outstanding Contribution in Reviewing, Journal of Natural Gas Science (2015) Research Fellowships: Venezuelan Foundation for Promotion of Researchers (2003, 2006, 2008) Teaching Excellence Nominee, University of Delaware (2005) Grants & Projects: Secured $800,000+ in funding, including NSF-supported studies on turbulent particle collisions ($359,861), naval additive manufacturing ($150,000), and solar energy systems ($158,162). Research collaborations span turbulence modulation, photovoltaic tech, and pipeline erosion.

Raja NagisettyView profile

Assistant Professor

• Environmental Engineering
• Hydrology
• Water Quality Modeling
• +4 more

Raja Nagisetty is an Assistant Professor in the Department of Environmental Engineering at Montana Tech's Lance College of Mines & Engineering. Holding a Ph.D. in Civil and Environmental Engineering from the University of Louisville (2009), he is a licensed Professional Engineer in Kentucky. Education: B.Tech (2000) and M.Eng (2002) from Indian institutions Professional Experience: 2014-present at Montana Tech, prior roles at University of Louisville and Indian institutions His research focuses on water quality modeling , stormwater management , and environmental health assessments , with recent work examining wildfire particulate matter impacts and mine waste remediation strategies. Current projects include modeling snowmelt runoff systems and evaluating geothermal seep interactions with river systems. Recent publications demonstrate expertise in dissolved oxygen dynamics , heavy metal characterization , and remote sensing applications . He serves on graduate councils and thesis committees, including chairs for Matt Strozewski and William George. Professional Affiliation: Montana American Water Resources Association Notable Award: Faculty Certificate of Appreciation from Sree Sastha Engineering & Technology (2004-2005) His work spans multiple environmental engineering domains, from drinking water infrastructure challenges to climate change adaptation strategies, with significant contributions to watershed management and pollution analysis.

Mehrdad MoharramiView profile

Assistant Professor

• Reinforcement Learning
• Markov Decision Processes
• Random Graphs
• +3 more

Mehrdad Moharrami is an Assistant Professor in the Department of Computer Science at the University of Iowa . He specializes in reinforcement learning, Markov decision processes, and random graph models for economics and computational systems. Education : BSc in Mathematics and Electrical Engineering from Sharif University of Technology; MSc in Electrical Engineering and Mathematics from University of Michigan; PhD in Electrical Engineering from University of Michigan (2020) His research focuses on robust reinforcement learning algorithms under distributional shifts and network structure analysis using parameterized random graphs. Recent work explores societal systems modeling and risk-sensitive control frameworks. Moharrami's publications demonstrate interdisciplinary expertise across computer science, mathematics, and network science , with notable contributions to: Risk-sensitive reinforcement learning (ICML 2025, Mathematics of Operations Research 2024) Random graph modeling (COLT 2025, Random Structures & Algorithms 2024) Network optimization (IEEE Transactions on Network Science 2024, IEEE Transactions on Information Theory 2023) Scientific recognition includes: Rackham Predoctoral Fellowship (2019) NSF MPS Workshop participation (2022) Multiple conference best paper nominations Iranian National Olympiad medals (2007-2008) As co-organizer of major workshops (SIGMETRICS 2022, Performance 2023), he actively contributes to academic community development while maintaining active referee roles for top journals like IEEE/ACM Transactions on Networking and Mathematics of Operations Research.

Michael L. KleinView profile

Professor

• Computational Chemistry
• Molecular Modeling
• Quantum Mechanics
• +4 more

Michael L. Klein is the Laura H. Carnell Professor of Science at Temple University. His research focuses on applying quantum mechanics and statistical thermodynamics to model molecular phenomena through advanced computational methods. He develops and adapts software for ab initio electronic structure calculations, molecular dynamics simulations, and novel hardware optimization (e.g., GPUs and multi-core CPUs). Collaborations span interdisciplinary teams addressing meso-scale biophysical systems and material interfaces. Research interests include: molecular simulations of complex systems, interface behavior (e.g., gold/water interfaces), adaptive surface functionalization, and protein-ligand interactions. Techniques emphasize enhanced sampling methods and coarse-grained modeling to bridge microscopic and macroscopic observations. Publications highlight contributions to surface spectroscopy, biomaterial design, and computational methodologies. His work bridges theory with experimental validation, often involving interdisciplinary partnerships in chemistry, materials science, and biophysics. Notable research directions include force field development for proteins (SPICA), understanding cannabinoid receptor dynamics, and modeling water behavior under various conditions. Software innovations focus on accelerating simulations for rare events and heterogeneous systems.

Rudolf SailerView profile

Lecturer

• Remote Sensing
• Glaciology
• Mountain Environmental Changes
• +3 more

Dr. Rudolf Sailer is a Lecturer and Academic Coordinator (BSc/MSc) at the Department of Geography, University of Innsbruck. His research focuses on Remote Sensing, Topographic LiDAR, and Mountain Environmental Changes, with a particular emphasis on glaciology, rockfall dynamics, and permafrost processes. He actively contributes to courses such as Statistical Exercises and coordinates bachelor's thesis seminars. Research interests include glacier mass balance, aerodynamic roughness modeling, and geomorphological processes in alpine environments. His work integrates multi-scale remote sensing data (e.g., Sentinel, LiDAR) and field observations to quantify environmental changes. He has collaborated on projects analyzing the Rofental research basin and the Khumbu Himal in Nepal. Recent publications address glacier mapping, rockfall activity, and snow cover dynamics using advanced remote sensing techniques. His lab, Remote Sensing & Topographic LiDAR, emphasizes interdisciplinary approaches to mountain environmental challenges.

Gurjeet SinghView profile

Researcher

• Soil Moisture Sensing
• Remote Sensing Applications
• Agricultural Drought Monitoring
• +3 more

Gurjeet Singh is a Researcher affiliated with the Department of Civil and Environmental Engineering (CEE) at Michigan State University’s College of Engineering. His work focuses on advancing soil moisture sensing technologies, remote sensing applications, and geospatial analysis for agricultural and environmental monitoring. He holds a position as a Research Associate, contributing to interdisciplinary projects involving satellite data integration (e.g., SMAP, Sentinel, NISAR missions) and precision agriculture solutions. His research interests include developing low-cost sensor networks, optimizing water resource management through satellite-derived soil moisture products, and analyzing drought patterns in regions like Senegal. He has pioneered methods for uncertainty estimation in soil moisture retrievals and has extensively studied the integration of SAR and optical remote sensing data for hydrological modeling. Recent studies emphasize multi-scale algorithm development for upcoming satellite missions, crop yield prediction under water stress conditions, and improving agricultural drought indices via remote sensing fusion. His work intersects environmental engineering, data science, and climate resilience strategies. Gurjeet collaborates on frameworks for drought hotspot identification, groundwater potential modeling, and precision irrigation optimization. His publications highlight technical innovations in sensor calibration, geostatistical analysis, and machine learning applications for hydrological forecasting. Despite no listed awards, his contributions are evident through impactful peer-reviewed outputs addressing global water and food security challenges.

Yiying TongView profile

Professor

• Discrete Geometric Modeling
• Physically-Based Simulation/Animation
• Discrete Differential Geometry
• +7 more

Yiying Tong is a Professor in the Department of Computer Science and Engineering at Michigan State University (MSU), within the College of Engineering. His research focuses on discrete geometric modeling, physically-based simulation/animation, and applications in biometrics, biomolecular surfaces, and medical imaging. Tong received his Ph.D. from the University of Southern California (2004), M.S. from Zhejiang University (2000), and B.Eng. from Zhejiang University (1997). Education: Ph.D., Computer Science, University of Southern California, 2004 M.S., Computer Science, Zhejiang University, China, 2000 B.Eng., Computer Science, Zhejiang University, China, 1997 Research Interests: Tong's work spans discrete differential geometry, computational geometry, fluid dynamics, and biomedical applications. Key areas include: Development of efficient algorithms for mesh generation and surface reconstruction Physically-based simulations for fluids, deformable objects, and sound Applications in biomolecular modeling, face recognition, and medical imaging Geometric modeling techniques for computer graphics and engineering Publications: His research emphasizes geometric and topological methods, with notable contributions to fluid simulation, meshing, and biomolecular surface modeling. Recent work includes advancements in Hodge decomposition, spectral graph theory, and manifold learning. Grants & Collaborations: Tong leads NSF-funded projects on geometric integrators and biomolecular modeling. Collaborators include institutions like Caltech, Zhejiang University, and the University of Southern California. His work bridges computational mechanics and computer graphics, with applications in bioinformatics and engineering. Labs & Teams: Supervises a research group focused on geometric computing, with active projects in discrete differential geometry, fluid simulation, and biomedical applications. Notable alumni include researchers at Qualcomm, Apple, and Google.

Subhash SuriView profile

Professor

• Algorithms and Theory
• Networking
• Computational Geometry
• +5 more

Subhash Suri is a Distinguished Professor in the Department of Computer Science at the University of California, Santa Barbara (UCSB). He holds academic ranks such as Fellow of ACM, AAAS, and IEEE, and is an ACM Distinguished Scientist. His research focuses on algorithms, computational geometry, sensor networks, robotics, and social networks. He directs the Applied Algorithms Lab and the Center for Geometric Computing at UCSB. Education: Ph.D. Computer Science, Johns Hopkins University, 1987 M.S. Computer Science, Johns Hopkins University, 1984 B.S. Electronics and Communication, IIT Roorkee, India, 1981 Research Interests: Prof. Suri's work emphasizes geometric and network algorithms, with applications in diverse fields. His lab develops foundational methods in computational geometry, graph theory, and algorithmic decision-making. Recent projects include dynamic geometric set cover, polychromatic TSP approximation, and shortest paths in complex environments. Awards: His honors include ACM/AAAS/IEEE Fellowships and recognition for contributions to algorithm design and analysis. Students/Grants: Advised over 20 graduate students, including notable alumni like Neeraj Kumar (Facebook) and Luca Foschini (Evidation Health). Active in NSF workshops and program committees (e.g., SoCG, WAFR, SWAT). Teaching: Recent courses include CS 130a/b (Data Structures/Algorithms), CS 190A (Algorithmic Decision Making), and CS 235 (Computational Geometry).

David M. RosenView profile

Assistant Professor

• Nonlinear Optimization
• Differential Geometry
• Topology
• +7 more

David M. Rosen is an Assistant Professor at Northeastern University, affiliated with the Departments of Electrical and Computer Engineering, Mathematics, and the Khoury College of Computer Sciences (by courtesy). He leads the Robust Autonomy Lab (NEU-RAL), focusing on mathematical and algorithmic foundations for trustworthy autonomous systems. ScD in Computer Science (2016), Massachusetts Institute of Technology MA in Mathematics (2010), University of Texas at Austin BS in Mathematics (2008), California Institute of Technology His research combines nonlinear optimization, differential geometry, abstract algebra, and probability to design robust algorithms for machine perception and control. Recent work emphasizes convex relaxation techniques for problems like SLAM and rotation averaging, enabling provably optimal solutions in real-world settings. Recent publications highlight advancements in range-aided SLAM, distributed pose-graph optimization, and spectral synchronization methods. These works often integrate semidefinite programming and Riemannian optimization to address non-convex challenges in autonomous navigation. 2023: Grant from Draper Laboratory for decentralized multi-agent perception 2023: Grant from MIT Lincoln Laboratory for certifiable perception tools His awards include the WAFR Best Paper Award (2016), RSS Pioneer Award (2019), RSS Best Student Paper Award (2020), and IEEE T-RO King-Sun Fu Award Honorable Mention (2021). He has contributed to key tools like SE-Sync and Shonan averaging, widely used in robotics and computer vision communities.

• Large Language Models
• Multilingual Machine Translation
• Vision-Language Models
• +3 more

Xiaocheng Feng is a prominent researcher in computational linguistics and large language models (LLMs), with extensive contributions to multilingual systems, vision-language integration, and knowledge alignment. His work spans from 2016 to 2025, focusing on advanced topics like Mitigating hallucinations in multimodal models Efficient knowledge editing across multiple models Length-controlled text generation for black-box LLMs Improving contextual faithfulness via retrieval heads Cross-lingual connection mechanisms for fine-tuning His recent publications address critical challenges in LLMs, including knowledge misalignment, entity-level unlearning, and Pareto optimization in multilingual translation. He has explored probability density estimation for text control, adaptive context modeling in visual storytelling, and cross-lingual annotation projection for low-resource languages. Collaborations with researchers like Bing Qin, Lei Huang, and Weitao Ma highlight his central role in advancing NLP methodologies.

Wang JunView profile

Assistant Professor

• Machine Learning
• Engineering Design
• Additive Manufacturing
• +10 more

Jun Wang is an Assistant Professor in the Department of Mechanical Engineering at Santa Clara University (SCU) . He received his Ph.D. and M.S. in Mechanical Engineering from SUNY Buffalo (2019, 2015) and a B.S. from Xi’an Jiaotong University (2012). He previously served as a Postdoctoral Associate in Mechanical Engineering at University of Maryland, College Park and worked in the IDEAL Lab, focusing on Engineering Design, Optimization, and Machine Learning. Education: Ph.D., Mechanical Engineering, SUNY Buffalo (2019) M.S., Mechanical Engineering, SUNY Buffalo (2015) B.S., Mechanical Engineering, Xi’an Jiaotong University (2012) Dr. Wang’s research lies at the intersection of Engineering Design , Machine Learning/Artificial Intelligence , Advanced Manufacturing , and Topology Optimization . His lab, GIFTED (Geometric Informatics For Technology, Engineering, and Design) , develops AI-driven methods for geometric modeling, metamaterial design, and additive manufacturing. Specific projects include inverse/generative design frameworks, surrogate models for manufacturing simulation, and high-resolution multi-scale material synthesis. His recent publications focus on machine learning integration with isogeometric analysis, implicit surface-based inverse design of cellular materials, and optimization of non-periodic metamaterials. Notable collaborations include work on vision transformers for image enhancement and generative adversarial networks for engineering applications. Trends in his research emphasize bridging geometric modeling with cloud computing and big data to accelerate design cycles. Scientific Awards: Best Paper Award (IDETC-CIE 2021) for non-periodic cellular design Dr. Wang mentors graduate researchers Shaoliang Yang (Ph.D. candidate) and Kevin Wang (Master’s graduate). His former students Matthew Franke (now at Intel) and Yunchen Li (now at Hymson USA Inc.) have transitioned to industry roles. His NSF-funded project ( $249,829 ) explores data-driven inverse design of additively manufacturable materials. At SCU, he leads the GIFTED Lab , which combines geometric modeling , machine learning , and advanced manufacturing to revolutionize design workflows. The lab’s work targets performance-equivalent designs generated via AI, with applications in aerospace, automotive, and biomedical industries.

Jason SimonView profile

Lecturer

• Fluid Dynamics
• Thermodynamics
• Atmospheric Modeling
• +3 more

Jason Simon is a Lecturer in the Department of Mechanical Engineering at the University of New Hampshire, College of Engineering and Physical Sciences. He teaches courses such as ESCI 502: Beaches and Coasts , ME 503: Thermodynamics , and ME 608: Fluid Dynamics . Education: Ph.D. in Civil and Environmental Engineering from University of California - Berkeley His research focuses on Fluid Dynamics , Thermodynamics , Atmospheric Modeling , and Land-Atmosphere Interactions . He has contributed to studies on turbulence modeling, weather prediction algorithms, and coastal hydrodynamics. Recent work includes leveraging LES simulations to study cloud development and subgrid-scale climate modeling. Publications highlight his expertise in land-surface heterogeneity , storm surge analysis , and numerical weather prediction . He has collaborated with researchers like A. D. Bragg and F. K. Chow on turbulence modeling and WRF-LES simulations.