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Pratyush TiwaryView profile

Professor

• Statistical Mechanics
• Computational Chemistry
• Molecular Dynamics
• +4 more

Pratyush Tiwary is the Millard and Lee Alexander Professor in Chemical Physics at the University of Maryland, College Park, with a joint appointment in the Department of Chemistry and Biochemistry and the Institute for Physical Science and Technology (IPST). He also leads the Therapeutics Discovery team at the Institute for Health Computing (UM-IHC). Tiwary’s research integrates statistical mechanics, molecular simulations, and artificial intelligence to study rare events in complex systems, including drug unbinding mechanisms, protein dynamics, and material nucleation. His work bridges disciplines, with affiliations spanning Chemical Physics, Biophysics, Physics, and Materials Science. Education: B.Tech. from IIT-BHU (2007), M.S. and Ph.D. in Materials Science from Caltech (2008–2012), followed by postdocs at ETH Zurich and Columbia University. He has taught courses in statistical mechanics, thermodynamics, and computational biochemistry, emphasizing interdisciplinary training. Research focuses on rare event simulation, machine learning-augmented molecular dynamics, and translational drug discovery. Key projects include developing RAVE (Reweighted Autoencoded Variational Bayes) for enhanced sampling and AlphaFold2-RAVE for protein ensemble generation. His lab collaborates with NIH and industry (e.g., Schrödinger) to advance AI-driven drug design and biomaterials research. Recognitions include the NSF CAREER Award, Sloan Research Fellowship, and multiple ‘Emerging Investigator’ accolades. He serves as an Associate Editor for Journal of Chemical Theory and Computation and on the Scientific Advisory Board of Schrödinger. Teaching and mentorship are central to his mission, with a commitment to fostering curiosity-driven research and equitable access to computational tools via open-source software and free coding bootcamps.

Suma BhatView profile

Assistant Professor

• Natural Language Processing
• Human-AI Interaction
• Computational Social Science
• +3 more

Suma Bhat is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the Coordinated Science Laboratory. She also holds affiliate faculty positions in Computer Science, Educational Psychology, the National Center for Supercomputing Applications, and the Center for Social and Behavioral Science. Her research focuses on natural language processing (NLP), human-AI interaction, computational social science, and medical language processing, with an emphasis on understanding linguistic nuances and enhancing user experiences in AI-driven systems. Her teaching includes courses like ECE 120 (Introduction to Computing), ECE 365 (Data Science and Engineering), and ECE 594 (Mathematical Models of Language). She received the 2021 ECE Ronald W. Pratt Faculty Outstanding Teaching Award. Her work explores parameter-efficient NLP methods, especially for idiosyncratic language phenomena like idioms and figurative expressions, and applies NLP to educational and medical contexts. Bhat leads the DIYA initiative, promoting data science education through camps and workshops, particularly for underrepresented groups. Recent efforts include expanding DIYA to India and collaborating on projects addressing societal challenges like climate change and healthcare equity. Her research group, ILEARN, investigates online learning dynamics and learner behavior, with notable contributions to automated assessment and medical education tools.

• Software Engineering
• Mobile Computing
• Large Language Models
• +3 more

Reyhaneh Jabbarvand Behrouz serves as an Assistant Professor at the Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign, where her research bridges software engineering theory and practical AI-driven solutions for modern computing challenges. Her primary research explores mobile application ecosystems, energy-aware software design, and the integration of large language models into development workflows. Key investigations include flaky test resolution, anomaly detection in distributed microservices, and optimizing mobile app energy consumption through novel testing frameworks. Publication trends reveal a strategic shift toward leveraging AI for software reliability, with recent work analyzing LLM reasoning in code execution and developing formal methods for workflow validation in cloud-native architectures. Scientific recognition includes: NSF CAREER Award (2023) Her NSF CAREER grant supports pioneering research in enhancing software testing robustness through machine learning, while her editorial leadership in the InteNSE workshop series establishes critical benchmarks for neural software engineering. Though student mentorship details aren't specified in source materials, her active publication record suggests ongoing guidance of graduate researchers in software engineering labs.

• Plasma Physics
• Computational Physics
• Physics Education
• +4 more

Christopher M. Orban is an Associate Professor at The Ohio State University Marion Campus in the Department of Physics. His research spans plasma physics, cosmological simulations, and innovative physics education methods. He leads the STEMcoding Project and develops free VR apps through the BuckeyeVR Initiative . Academic Rank: Associate Professor Department: Physics University: The Ohio State University Campus: Marion Campus Research Interests: Orban investigates high-intensity laser-plasma interactions for ion acceleration, develops computational tools for astrophysical simulations, and pioneers the use of virtual reality and programming exercises in physics education. His work bridges theoretical modeling with experimental validation. Laser-Produced Plasmas Cosmological Perturbation Theory Virtual Reality Education Computational Thinking Code Validation (FLASH, Gadget2) Electromagnetic Field Simulations Notable Contributions: He developed open-source educational tools like the Arduino-based pressure sensor and created browser-based physics simulations using classic video games. His 2020 High Energy Density Physics paper validated FLASH code for astrophysical jet modeling, while 2019 Physics Education work explored computational thinking in introductory courses.

Narayan B. MandayamView profile

Professor

• Resilience in Smart Cities
• Prospect Theory in Cyberphysical Systems
• Privacy and Security for IoT
• +6 more

Narayan B. Mandayam is a Professor and Director of the Wireless Information Network Laboratory (WINLAB) at Rutgers University's Department of Electrical and Computer Engineering. He previously served as Chair of the department from 2016 to 2022. His research focuses on smart city resilience, IoT security, energy-efficient systems, and game-theoretic models for cyber-physical systems. Located in the Computing Research & Education Building (CoRE), he leads interdisciplinary projects addressing societal challenges in communication networks and infrastructure security. Education : B.Tech. (Hons) Electrical Engineering, Indian Institute of Technology, Kharagpur (1989) M.S. and Ph.D. Electrical Engineering, Rice University, Houston TX (1991–1994) Research Interests include: Prospect Theory applications in cloud security and smart grids Noncontiguous spectrum access and software-defined networks Modeling social knowledge creation on the internet Visual MIMO networks and metamaterial-based security His work bridges economics, machine learning, and wireless systems, with recent emphasis on 6G and IoT resilience. Key Awards : 2015 IEEE Communications Society Advances in Communications Award 2014 IEEE Donald G. Fink Award 2009 IEEE Fred W. Ellersick Prize 2018 Indian Institute of Technology Distinguished Alumnus Award His research outputs span 5G/6G innovations, security protocols using metamaterials, and game-theoretic models for adversarial scenarios. Current trends include leveraging AI for network optimization and mitigating radio interference impacts on weather forecasting. Lab & Leadership : As WINLAB Director, he oversees cutting-edge projects in wireless systems, collaborating with industry and government entities. His teams develop solutions for smart grid resilience, autonomous vehicle security, and federated learning incentivization.

Victoria DeanView profile

Assistant Professor

• Robotics
• Machine Learning
• Reinforcement Learning
• +5 more

Victoria Dean (she/her) is an Assistant Professor of Computer Science at Olin College of Engineering , where she investigates robotics and machine learning with a focus on exploration strategies , benchmarking , and democratizing AI . Her work includes the TOTO Benchmark for real-robot evaluation and audio-visual curiosity models for exploration. Education : Ph.D. in Robotics (Carnegie Mellon, 2023), B.S. in Computer Science and Engineering (MIT, 2017) Dean's research bridges robotics and machine learning , emphasizing task-agnostic exploration , domain adaptation , and ethical AI curriculum development . She co-designed Train Offline, Test Online (TOTO) , a remote robot evaluation platform with multimodal dataset access. Her scientific accolades include a NeurIPS Best Paper Award (2022), NSF GRFP (2020), and Siebel Scholar recognition (2022). She organized workshops like L-DOD at ICRA 2023 and Robot Learning in the Cloud at RSS 2022. Dean actively promotes undergraduate research and diversity in STEM . At Carnegie Mellon, she served as Instructor of Record for Ethics and Robotics (16-735) and Teaching Assistant for Deep Reinforcement Learning (16-881). At MIT, she co-founded Code for Good and taught courses including Introduction to Electrical Engineering (6.01).

Yue JinView profile

Assistant Professor

• Thermal-Fluid Sciences
• Nuclear Engineering
• Heat Transfer
• +7 more

Yue Jin is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the University of Missouri (Mizzou), where he also serves as the director of the Advanced Flow and Heat Transfer Lab (AFHTL). He previously held a postdoctoral research associate position at MIT before joining Mizzou in 2022 as part of a cohort of 14 new faculty members. His work is closely tied to the MU Research Reactor (MURR), the highest-powered university research reactor in the U.S., which provides a unique platform for his research in nuclear thermal hydraulics and clean energy systems. PhD, Pennsylvania State University (2019) MS, Shanghai Jiao Tong University BS, Xi’an Jiaotong University Dr. Jin's research focuses on thermal-fluid sciences and nuclear engineering, particularly in fluid flow, heat and mass transfer, reactor thermal hydraulics, and the modeling of advanced energy systems such as compact heat exchangers and next-generation nuclear reactors. He is deeply involved in the development and validation of multi-scale, multi-physics numerical tools enhanced by artificial intelligence and machine learning. His lab conducts high-resolution experiments using advanced imaging techniques like S-PIV/LIF and laser diagnostics to study phenomena such as critical heat flux (CHF), droplet dynamics during reflood transients, and two-phase flow behavior in rod bundles. His recent publications reveal a strong trend toward integrating physics-informed machine learning with traditional thermal-hydraulic modeling to improve prediction accuracy in complex systems. Topics include CHF modeling, reflood safety analysis, accident-tolerant fuels, and thermal striping in advanced reactors. His work bridges experimental validation with code development, particularly using tools like COBRA-TF and TRACE. Development of Innovative Physics-Informed Data-Driven Model for COBRA-TF CHF Prediction (MU Research Council) Advanced Reflood Thermal-Hydraulics for Uncertainty Resolution (NRC/Penn State) Midwest Industrial Assessment Center (Department of Energy) Massive Reflood Data Evaluation Using Machine Learning (NRC/Penn State) Dr. Jin actively collaborates with researchers at MIT, Penn State, and General Atomics. He mentors graduate students and welcomes both graduate and undergraduate researchers to his lab. His research contributes to the global effort in clean energy sustainability and next-generation nuclear safety.

Morgan GreenView profile

Assistant Professor

• Creative Computation
• Algorithmic Poetry
• New Media Art
• +7 more

Morgan Green is an Assistant Professor in the Department of Computer Science at Allegheny College, where she integrates art and computation in her teaching and research. She holds an MFA from the School of the Art Institute of Chicago and a BA from the University of Southern California. She also teaches creative computation at the University of Illinois at Chicago and is a resident artist in the New Media Program at Mana Contemporary, Chicago. Master of Fine Arts, School of the Art Institute of Chicago Bachelor of Arts, University of Southern California Green’s research and artistic practice explore the intersections of language, technology, and identity. Her work centers on algorithmic poetry, human-machine interaction, and queer cyborg theory, often using code to generate poetic texts and performances that challenge normative understandings of communication and embodiment. She investigates how digital systems reflect and shape social identities, particularly through the lens of gender and sexuality. Her publications and artworks, such as autocanon , hold you/mold you , and House of Words , demonstrate a consistent engagement with generative systems, textual transformation, and performative computing. These works span interactive web installations, live performances, and critical essays, revealing a deep commitment to making technology accessible and critically reflective. House of Words – Critical Coding Cookbook Comparing the Incomparable – Toward Data Science hold you/mold you – Interactive browser-based art autocanon – Algorithmic poetry performance Green has no listed scientific awards in the provided texts, but her work has been featured in significant exhibitions and publications, indicating recognition in the art and digital humanities communities. She mentors through participatory projects like Little Readings , where she wrote custom poems for audience submissions, and she receives funding from institutions like The New School. Her teaching at UIC and Allegheny College suggests active engagement in curriculum development and student mentorship in creative computing. Green is a core artist in the Mana Contemporary New Media Program, where she develops and performs works that merge digital systems with embodied experience. Her studio practice involves building 'poetry machines' and exploring feedback loops between human and algorithmic creativity, often in collaborative or public settings.

Cynthia RushView profile

Associate Professor

• Mathematical Statistics
• High-Dimensional Inference
• Approximate Message Passing
• +7 more

Cynthia Rush is an Associate Professor in the Department of Statistics at Columbia University, where she conducts research at the intersection of mathematical statistics, information theory, and high-dimensional data analysis. She is affiliated with the Graduate School of Arts and Sciences and is based in the School of Social Work building in New York City. Ph.D. in Statistics, Yale University, 2016 B.S. in Mathematics, University of North Carolina at Chapel Hill Her research focuses on the development and analysis of algorithms for high-dimensional inference, particularly using Approximate Message Passing (AMP), with applications in sparse regression, signal processing, and machine learning. She integrates tools from information theory, statistical physics, and applied probability to understand fundamental limits and performance of modern statistical methods. Her recent publications span topics such as SLOPE regularization, community detection, power posteriors, and massive multiple access in communications. These works appear in top journals including IEEE Transactions on Information Theory, Annals of Statistics, and NeurIPS. Her research shows a consistent trend toward rigorous theoretical analysis of high-dimensional statistical algorithms and their practical implications. Notable scientific recognition includes being a finalist for the JKW Student Paper Award at IEEE ISIT in 2016. She has delivered numerous invited talks at leading institutions and conferences worldwide, reflecting her active role in the theoretical data science community. JKW Student Paper Award Finalist, IEEE ISIT 2016 She advises and collaborates with researchers in statistics, machine learning, and information theory, though formal doctoral advisees are not listed. Her work is supported by theoretical grants and collaborations across engineering and mathematical sciences. She is involved in the broader academic community through seminars, workshops, and program committees, contributing to the advancement of rigorous statistical learning. Cynthia Rush leads a research group focused on high-dimensional inference and algorithmic analysis, often collaborating with researchers at institutions like Yale, Princeton, and international centers in Europe and Asia. Her lab emphasizes theoretical foundations with practical relevance in data science and communications.

Zhu LiView profile

Professor

• Point Cloud Compression
• Light Field Compression
• Deep Learning in Visual Compression
• +4 more

Zhu Li is a full professor at the Department of Computer Science & Electrical Engineering (CSEE) at the University of Missouri, Kansas City (UMKC), and director of the NSF Center for Big Learning (CBL). He holds a PhD in Electrical & Computer Engineering from Northwestern University (2004). Previously, he served as an AFRL summer visiting professor at the US Air Force Academy (2016-18, 2020-24), Senior Staff Researcher at Samsung Research America and FutureWei Technology, and Assistant Professor at The Hong Kong Polytechnic University. His research focuses on point cloud and light field compression, deep learning for visual compression, remote sensing, and multimedia systems. Key achievements include 50+ patents and 200+ publications. He is an IEEE Senior Member and serves as Associate Editor-in-Chief for IEEE Trans on Circuits & System for Video Technology, and Associate Editor for IEEE Trans on Image Processing and others. Notable awards include the Best Paper Runner-up at PBVS 2023, Best Poster at ICME 2006, and Best Paper at ICIP 2007. His lab has secured grants from NSF, AFRL, and industry partners, advancing research in neural compaction, SAR image processing, and immersive media communication. Education: PhD in Electrical & Computer Engineering, Northwestern University (2004) Affiliations: NSF I/UCRC Center for Big Learning (Director), IEEE (Senior Member) Grants: NSF Trustee's N.T. Veatch Award (2025), AFRL Phase 2 STTR ($1.2M), NSF 5G/6G Networks Grant (2022) Labs/Teams: Multimedia Computing & Communication Lab, MCC Lab collaborating with global institutions

• Interactive Data Visualization
• Intelligence Augmentation
• Machine Learning Interpretability
• +3 more

Arvind Satyanarayan is an Associate Professor of Computer Science at MIT, leading the Visualization Group within MIT CSAIL. His research focuses on intelligence augmentation through interactive data visualization, exploring how computational tools can amplify human cognition and creativity while respecting user agency. He holds a PhD from Stanford University, advised by Jeffrey Heer. Key research themes include visualization design tools, machine learning interpretability, accessible visualization for visually impaired users, and behavioral principles for information systems. His work has been recognized with an NSF CAREER Award and a 2024 Alfred P. Sloan Fellowship, alongside best paper awards at CHI and IEEE VIS. Education: PhD in Computer Science from Stanford University (Interactive Data Lab) Notable Achievements: Developed widely-used systems like Altair and Vega-Lite, impactful in data science communities Advising: Mentors over 20 graduate and undergraduate researchers, with notable alumni advancing to faculty roles at Brown CS and Utah CS Recent projects include tactile visualization systems (Tactile Vega-Lite), semi-formal programming frameworks (Pluto), and cultural interpretability of AI models. His lab emphasizes collaboration with diverse stakeholders to ensure inclusive design practices.

Spencer LaVere SmithView profile

Associate Professor

• Neuroengineering
• Computational Neuroscience
• Neural Circuitry
• +3 more

Spencer LaVere Smith is an Associate Professor in the Department of Electrical and Computer Engineering at UC Santa Barbara (UCSB). He leads the SLAB (Spencer Lab) focusing on neuroengineering and computational neuroscience. His research combines novel instrumentation design with experimental insights into neural circuit function. Dr. Smith holds a B.S. in Physics and Mathematics from the University of Iowa, and a Ph.D. in Neuroscience and Neuroengineering from UCLA. After postdoctoral work at University College London, he established his laboratory in the US in 2011, moving to UCSB in 2018. His honors include the Klingenstein-Simons Fellowship (2013), McKnight Technological Innovation Award (2015), and Presidential Early Career Award for Scientists and Engineers (2019). Research interests center on developing tools for measuring/manipulating neural activity, including multiphoton microscopy innovations and deep-brain recording systems. The lab explores mesoscale neural circuit architecture, visual processing dynamics, and neurotechnology applications for understanding brain-behavior relationships. Key areas of focus include: Developing next-generation multiphoton imaging systems with expanded field-of-view capabilities Exploring neural manifold topology in population activity Investigating cortical communication through discrete information channels Advancing neurophotonic tools for high-speed neural recording His work bridges engineering and neuroscience, with recent advancements in Air Objective Imaging and Neuroformer generative models for brain data analysis. Current projects emphasize real-time neural circuit visualization and translational applications of neurotechnology.

Andrés GoensView profile

Assistant Professor

• Programming Languages
• Formal Methods
• Theorem Proving
• +3 more

Andrés Goens is an Assistant Professor at the University of Amsterdam since 2023. He holds a Ph.D. (Dr.rer.nat.) in Computer Science from TU Dresden (2021) and an M.Sc. in Mathematics from RWTH Aachen University (2014). His research focuses on programming languages, formal methods, and theorem proving, with applications to compilers and heterogeneous systems. He investigates efficient execution of concurrent programs in multicore architectures and explores machine learning techniques for compiler optimization. Key research areas include concurrency, weak memory models, and bridging abstract mathematics with practical compiler design. His work often employs theorem provers like Lean to formalize program behavior. Recent publications address e-graphs for variable handling, guided equality saturation, and optimizing virtual networks in distributed systems. Publications span venues like PLDI, POPL, and ISCA, reflecting contributions to programming language theory and compiler infrastructure. His research bridges theoretical foundations with practical system design, aiming to improve both programmer productivity and computational efficiency. Contact details: a.goens@uva.nl (professional) and andres@goens.org (personal).

Gregory M. KapfhammerView profile

Associate Professor

• Software Engineering
• Software Testing
• Computer Systems
• +3 more

Gregory M. Kapfhammer is an Associate Professor in the Department of Computer and Information Science at Allegheny College. His expertise spans software engineering , software testing , and computer systems , with a focus on automated testing techniques, mutation testing, and educational tools for programming. Education: Ph.D. and M.S. in Computer Science from the University of Pittsburgh, B.S. in Computer Science from Allegheny College. His research emphasizes software testing , including flaky tests , mutation testing , and regression testing . He has developed open-source tools like Chasten and SchemaAnalyst to improve software quality and educational practices. Recent publications address systemic flakiness and pseudo-testedness in test suites. Gregory actively contributes to the academic community as an associate editor for the Journal of Software: Evolution and Process and academic editor for PeerJ Computer Science. He co-hosts the popular Software Engineering Radio podcast, interviewing leaders in AI-assisted coding and terminal interface design. His open-source projects, such as GatorGrader and Cellveyor , demonstrate his commitment to advancing software education and development workflows. He has presented at events like PyCon, focusing on Python-based automated assessment tools and agentic coding interfaces.

Oscar ChaparroView profile

Associate Professor

• Software Engineering
• Software Maintenance & Evolution
• Program Comprehension
• +13 more

Oscar Chaparro is an Associate Professor of Computer Science at William & Mary, where he leads the Software Evolution and Analysis (SEA) Lab. He holds tenure effective Fall 2025 and is currently on sabbatical during the 2025–2026 academic year. Education: Ph.D. in Software Engineering, The University of Texas at Dallas, advised by Dr. Andrian Marcus B.Eng. and M.Eng. in Systems Engineering and Computing, Universidad Nacional de Colombia, Bogotá, Colombia Oscar Chaparro's research focuses on improving software maintenance and evolution through empirical and automated methods. His work spans automated bug report management, verification-guided refactoring, informed decision-making for software changes, and software licensing and supply chain management. He integrates techniques from program analysis, software repository mining (MSR), natural language processing (NLP), machine learning, and computer vision to analyze software artifacts such as bug reports, source code, and developer discussions. His research aims to enhance developer productivity, software quality, and legal compliance in modern software development. His recent publications (2023–2025) demonstrate a strong trend in addressing real-world software engineering challenges, particularly in bug localization, duplicate detection, interactive reporting tools, and the emerging domain of quantum and AI-related software issues. His work often involves collaboration with industry and interdisciplinary perspectives, including legal aspects of software. Scientific Awards and Recognitions: Distinguished Reviewer Award at ICPC'25 Student awards: Charles Center Summer Research Grant (Sam), 2025 International Student Achievement Award (Antu) Oscar actively mentors students, including Antu Saha, Nathan Cooper, and Trevor Stalnaker, who frequently co-author his publications. He has received research funding and institutional support, enabling student-led projects and tool development. He is involved in organizing events such as the NLBSE Tool Competition. Labs and Teams: He leads the Software Evolution and Analysis (SEA) Lab, which conducts cutting-edge research in software engineering, focusing on practical tools and empirical studies to support developers throughout the software lifecycle.