Dr. Hoontaek Seo serves as Associate Professor of Finance and Chair of the Department of Accounting, Economics, and Finance at Niagara University's Holzschuh College of Business Administration, where he has been faculty since fall 2010. His office is located in Bisgrove Hall Room 354F with office hours Tuesdays and Thursdays 10 a.m.-12 p.m. and Wednesdays 1:30-3:30 p.m. He holds a Ph.D. in Finance from Michigan State University (2009), forming the foundation of his academic expertise. Research focuses on empirical corporate finance mechanisms, particularly security offerings dynamics, corporate governance structures, control systems, and divestiture strategies. His work examines how these elements influence corporate financial decision-making and market valuation processes within empirical frameworks.
Joel Andreas is a Professor of Sociology at Johns Hopkins University since 2003, serving as Program Director of the East Asian Studies Program. His research focuses on political contention, social inequality, and social change in contemporary China. He holds a PhD in Sociology from UCLA and teaches social theory, political sociology, and courses on Chinese society. He has held visiting positions at universities including the University of Sydney, Hong Kong University, and Ningxia University. His work examines labor movements, rural land policies, and class dynamics in China. Recent research includes analyses of staff-worker congresses, rural land dispossession, and the persistence of militarism in U.S. policy. He critiques market reforms and explores the legacy of the Cultural Revolution. Andreas has contributed to debates on China’s transition to capitalism and the decline of socialist systems. His publications span topics from urbanization to state coercion in rural development. He maintains an active role in academic exchanges and policy-relevant research on China’s socio-political landscape.
Wei Xu is an Associate Professor at Georgia Institute of Technology's College of Computing and School of Interactive Computing, with affiliations to the Machine Learning Center. Their research bridges machine learning, natural language processing, and social media with focus areas in large language models, cultural bias mitigation, multilingual capabilities, and human-AI collaboration in text evaluation. NSF CAREER and Google Academic Research Award recipient Director of NLP X Lab PhD from New York University, BSMS from Tsinghua University Research interests span: Multilingual Multicultural LLMs addressing representational gaps and cultural adaptation in language models (NAACL 2025, ACL 2024); Robustness and Reasoning through dynamic AGI evaluations (ACL 2024, EMNLP 2024); Interdisciplinary NLP applications in security, healthcare, and law (EMNLP 2024, ACL 2024). Recent publications focus on multilingual alignment (NAACL 2025), privacy risk estimation (arXiv 2025), cultural bias analysis (ACL 2024), and medical text simplification (EMNLP 2024). Key themes include bias mitigation, multimodal processing, and practical LLM evaluation. Scientific Awards : NSF CAREER, Google/Sony/Criteo research awards, ACL'24 Best Social Impact Award, COLING'18 Best Paper Advising 15 PhD/MS/BSMS students including Yao Dou (human-centered LLM evaluation), Tarek Naous (multilingual LLMs), and alumni like Chao Jiang (Apple AI/ML) and Yang Chen (NVIDIA research scientist). Teaches graduate courses on NLP and LLMs.
Jennifer Ross is a Professor of Physics and Associate Dean for Creativity, Scholarship, and Research at the College of Arts & Sciences of Syracuse University . As a biophysicist, she investigates how cells organize their interiors through self-assembly and active matter principles, focusing on the microtubule cytoskeleton and enzyme-driven systems using single-molecule imaging . Her research bridges fundamental physics with biological organization . Education: Ph.D. in Physics, University of California, Santa Barbara (2004) B.A. in Physics and Mathematics, Wellesley College (2000) Research Focus: Self-organization of cytoskeletal networks Active matter dynamics in biological systems Motor protein interactions and cargo transport Programming circadian materials via biomolecular systems Microtubule severing mechanisms Recent Article Trends: 2025 studies explore kinesin-driven cytoskeletal composites, urease-DNA origami engineering, and crosslinker-regulated network mechanics 2024-2023 work examines ionic strength effects on microtubules, programmable circadian materials, and motor-cargo dynamics Earlier studies analyze actin-microtubule composites, liquid crystal phase control, and severing enzyme mechanisms Scientific Awards: Fellow of the American Physical Society (APS) and American Association for the Advancement of Science (AAAS) Cottrell Scholar (2025) and STAR Award Margaret Oakley Dayhoff Award (Biophysical Society) Grants: Leads multiple NSF, Sloan Foundation, and Research Corporation grants for projects like "Energy and Entropy Sculpting" and "Explorations: SUPER-Tech SHIP" . Teaching: Offers courses in experimental physics, microscopy, and biophysics, including a globally adopted hands-on microscope-building curriculum. Lab: Heads the Bio-Active Matter Lab , studying how cells harness noisy systems for autonomous organization.
Stephen Licht is an Associate Professor of Ocean Engineering and Graduate Director at the University of Rhode Island's College of Engineering, where he directs the Robotics Laboratory for Complex Underwater Environments (R-CUE). His research focuses on developing maritime robots capable of operating in dynamic and unpredictable environments through biologically inspired propulsion, distributed pressure sensing, model-based optimal control, and compliant underwater manipulation technologies. Ph.D. in Oceanographic and Mechanical Engineering from MIT/WHOI Joint Program (2008) B.S. in Mechanical Engineering from Yale University (1998) Former Senior Research Scientist at iRobot and Senior Robotics Engineer at Vecna Robotics Current Research Affiliate with MIT Department of Mechanical Engineering Former Visiting Faculty at Libera Università di Bolzano (2019-2020) Dr. Licht's research spans marine robotics with emphasis on biologically inspired propulsion systems that provide high authority and bandwidth thrust, nonlinear attitude control for maneuvering in dynamic conditions, compliant underwater manipulation technologies, and unmanned aerial monitoring of coastal structures. His work bridges mechanical engineering principles with oceanographic applications to create more capable underwater robotic systems that can operate in complex marine environments. His recent publications demonstrate a strong trend toward soft robotics applications for deep-sea exploration, with particular focus on jamming grippers and neutrally buoyant manipulation systems. The research also shows increasing integration of additive manufacturing techniques for field-deployable solutions and computational methods for autonomous systems operating in challenging marine environments. His work spans fundamental control theory, mechanical design, and practical field applications. Dr. Licht has secured significant research funding as both Principal Investigator and Co-Principal Investigator from major organizations including the Office of Naval Research, NOAA, NSF, and various university collaborations. His grants focus on advancing unmanned underwater vehicle technology, soft robotics for deep-sea applications, and coastal monitoring systems. Active mentor to numerous graduate and undergraduate students in Ocean Engineering Successful track record of student placements at organizations including Jaia Robotics, Scripps Institution of Oceanography, FORSSEA Robotics, and government research labs Collaborates with researchers at MIT, WHOI, University of Connecticut, University of Maine, and international institutions Licht leads the R-CUE lab which develops innovative solutions for underwater robotics challenges, with particular expertise in biomimetic propulsion, soft robotics for deep-sea applications, and autonomous systems for environmental monitoring. The lab maintains strong industry connections with OceanGate Inc. and FabNewport, and engages with local educational institutions through outreach programs with Roger Williams Middle School.
Minh Q. Phan is an Associate Professor of Engineering at Dartmouth College's Thayer School of Engineering. His expertise spans system identification, iterative learning control, model predictive control, robotic swarm control, and intelligent control systems. He holds a BS from the University of California, Berkeley, and MS/M.Phil/PhD degrees from Columbia University in Mechanical Engineering. Dr. Phan has contributed to over 50 peer-reviewed publications and serves as an Associate Editor for the Journal of Guidance, Control, and Dynamics. His research focuses on advancing control theory applications in robotics, structural health monitoring, and sustainable construction materials. Key contributions include the development of OKID (Observer/Kalman Filter Identification) methods and bilinear system identification frameworks. Education History: Bachelor of Science in Mechanical Engineering, UC Berkeley, 1985 Master of Science in Mechanical Engineering, Columbia University, 1986 Master of Philosophy in Mechanical Engineering, Columbia University, 1988 Doctor of Philosophy in Mechanical Engineering, Columbia University, 1989 Research Interests: Advanced control methodologies for dynamic systems Model-based predictive control strategies Applications in robotics and aerospace engineering Structural health monitoring via system identification Machine learning for materials science Teaching Responsibilities include courses like ENGG 149 (Systems Identification), ENGS 145 (Modern Control Theory), and ENGG 148 (Structural Mechanics). His work bridges theoretical control systems with practical industrial applications, including automation in food processing and sustainable construction practices. Dr. Phan has collaborated on projects addressing viral epidemiology in Vietnam and coastal erosion mitigation strategies.
Massachusetts Institute of TechnologyUnited States
Nadia Figueroa is the Shalini and Rajeev Misra Presidential Assistant Professor in the Mechanical Engineering and Applied Mechanics (MEAM) Department at the University of Pennsylvania. She holds secondary appointments in Computer and Information Science (CIS) and Electrical and Systems Engineering (ESE), and is a core faculty member at the GRASP Lab. Prior to Penn, she was a Postdoctoral Associate at MIT’s CSAIL and earned her PhD in Robotics from EPFL under Prof. Aude Billard. Her research focuses on physical and perceptual adaptive intelligence for robots, enabling fluid collaboration with humans in dynamic environments. Key applications include robot learning from demonstration , human-robot co-manipulation , safe navigation in human-centric spaces , and rehabilitation robotics . Her work integrates machine learning control theory artificial intelligence biomechanics psychology with guarantees of stability, safety, and robustness . Recent publications highlight advancements in reactive collision avoidance dynamical system learning intent estimation EEG-driven assistive control origami-based reconfigurable robots across platforms like autonomous vehicles and humanoid robots. She has authored a 2022 textbook on dynamical systems for robot control and received the Presidential Assistant Professorship at Penn.
University of California , Santa Barbara (UCSB)United States
Professor Omar A. Saleh is a distinguished physicist and materials scientist at the University of California, Santa Barbara, holding appointments in both the Materials and Physics Departments. Since summer 2023, he has served as Chair of the Materials Department and maintains a minority appointment in the Biomolecular Science and Engineering (BMSE) Program, where he previously served as Director from 2013-2017. His educational background includes a B.S. in Physics from MIT (1997) and a Ph.D. in Physics from Princeton (2003), supported by a Hertz Fellowship. Following postdoctoral work at École Normale Supérieure in Paris developing single-molecule techniques for motor protein/DNA studies, he joined UCSB in 2005. Saleh's research centers on fundamental principles of biomolecular behavior through experimental investigation of biopolymer elasticity and biomimetic organelles. His lab pioneers precision single-molecule stretching experiments to study entropic/energetic contributions in soft systems and creates life-like behaviors using reconstituted nucleic acid/protein assemblies. Key focus areas include DNA nanostar phase separation, liquid-liquid phase behavior, intrinsically disordered proteins, and non-equilibrium biomolecular systems. His publication trends reveal a strong emphasis on biomolecular condensates (2023-2025), with recurring themes in DNA nanotechnology, polyelectrolyte physics, and single-molecule mechanics. Recent work explores tension-mediated control of phase separation, transcriptional regulation of biomolecular liquids, and active matter principles in DNA systems. NSF CAREER Award (2008) Bessel Research Award from Alexander von Humboldt Society (2017) Fellow of the American Physical Society (2019) Saleh actively mentors graduate students and postdocs including Sam Wilken, Gabrielle Abraham, Anna Nguyen, and Aria Chaderjian, whose research spans DNA nanostar liquids, active droplets, and complex coacervation. His lab develops innovative instrumentation including high-speed magnetic tweezers and GPU-based tracking systems, supported by grants such as NSF/MCB-BSF: Direct force measurements of intrinsically disordered proteins. The Saleh Group operates at BioE 3006, focusing on creating quantitative models of biological function through physical reconstitution.
Amitabha Bagchi is a Professor in the Department of Computer Science and Engineering at IIT Delhi. His research spans data algorithmics, probability, networks, and theoretical computer science, with applications in distributed systems, social networks, and AI-driven platforms. He has published extensively in leading venues such as SIGMOD, VLDB, ICDE, AAAI, and KDD, often collaborating with students and researchers on problems involving graph algorithms, fairness, and large-scale data analysis. Research Interests: His primary research interests include Data Algorithmics, Probability and Networks, Theoretical Computer Science, Distributed Algorithms, Graph Algorithms, and Machine Learning Theory. He investigates algorithmic foundations for real-world problems such as food delivery optimization, social network analysis, and efficient data structures for streaming and large graphs. Publication Trends: Recent publications focus on fairness in gig economy platforms, efficient solvers for graph Laplacians, generalization in neural networks, and temporal graph querying. His work combines theoretical rigor with practical impact, often involving GPU acceleration, distributed computing, and data-aware algorithm design. Scientific Service: Editor, Algorithms (2020–present) Editor, Journal of Discrete Algorithms , Elsevier (2006–2018) Guest Editor, special issue on Algorithms for Shortest Paths in Dynamic and Evolving Networks , Algorithms (2021) Volume Editor for proceedings of ESA, ATMOS, COCOON, and others Conference Leadership: He has served on numerous program committees and as chair for conferences including ESA (Engineering Track, 2016), ATMOS (2020), and ICALP (2019). His involvement spans algorithmic engineering, transportation optimization, and theoretical computer science forums. Teaching: He currently teaches COL863: Special Topics in Theoretical Computer Science on concentration inequalities and their applications. He has previously taught advanced courses in algorithms and data structures.
Samuel J. Gershman is a Professor of Psychology at Harvard University, affiliated with both the Department of Psychology and the Center for Brain Science. He directs the Computational Cognitive Neuroscience Lab (CCNLab), where he investigates how the brain acquires richly structured knowledge about the environment and uses this knowledge to guide adaptive behavior. Gershman received his B.A. in Neuroscience and Behavior from Columbia University in 2007 and his Ph.D. in Psychology and Neuroscience from Princeton University in 2013, followed by postdoctoral training in the Department of Brain and Cognitive Sciences at MIT (2013-2015). His research spans computational neuroscience, cognitive psychology, and machine learning. His primary research interests include learning, memory, decision making, and computational neuroscience. Gershman's work integrates behavioral, neuroimaging, and computational techniques to understand cognitive processes. He has made significant contributions to understanding memory systems, reinforcement learning, and the computational principles underlying human cognition. Analysis of Gershman's recent publications reveals a strong focus on computational approaches to understanding cognitive processes, with particular emphasis on memory systems, decision-making mechanisms, and the intersection of artificial intelligence with cognitive neuroscience. His work often bridges theoretical computational models with empirical neuroscience data, exploring how the brain implements efficient cognitive algorithms. Gershman actively mentors graduate students and postdoctoral researchers, with current advisees working on diverse projects spanning computational modeling, neuroimaging, and behavioral experiments. His lab investigates topics ranging from dopamine signaling to social cognition using a combination of theoretical and experimental approaches. The CCNLab, which Gershman directs, brings together researchers from psychology, neuroscience, computer science, and related fields to explore the computational principles of cognition. The lab utilizes a range of methodologies including behavioral experiments, neuroimaging, computational modeling, and theoretical analysis to address fundamental questions about how the mind works.
Kenneth S. Breuer is a Professor of Engineering at Brown University, serving as Director of the Center for Fluid Mechanics. He holds appointments in the School of Engineering and collaborates across disciplines, including Biology and Physics. His research focuses on fluid mechanics, animal flight mechanics (particularly bats), bacterial motility, renewable energy, and turbulence. Breuer earned his Sc.B. from Brown University and M.Sc./Ph.D. from MIT, returning to Brown in 1999 after faculty service at MIT. Education: Sc.B. (Brown), M.Sc./Ph.D. (MIT). Awards include Fellowships from the American Physical Society and American Society of Mechanical Engineers, and the Harold and Esther Edgerton Chair at MIT. He has authored over 100 publications and edited books such as *Microscale Diagnostic Techniques*. Research Interests: Fluid mechanics at micro/nanoscales, bio-inspired flight mechanisms, energy harvesting, and vortex dynamics. Collaborations include Professors Sharon Swartz (Biology) and Thomas Powers (Engineering). Current projects explore bat wing aerodynamics, membrane hydrofoils, and aerosol transmission in vehicles. Awards: APS Division of Fluid Dynamics Chair (2012), Midwest Mechanics Lecturer, and multiple fellowships. Teaching includes courses in Fluid Mechanics, Transport Phenomena, and Renewable Energy Systems. His lab develops bio-inspired robotic systems and studies flow interactions in animal and engineered systems.
Massachusetts Institute of TechnologyUnited States
Tommi Jaakkola is the Thomas Siebel Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society at the Massachusetts Institute of Technology. He received his MSc in theoretical physics from Helsinki University of Technology in 1992 and his PhD from MIT in computational neuroscience in 1997. After completing a postdoctoral position in computational molecular biology as a DOE/Sloan fellow at UCSC, he joined the MIT EECS faculty in 1998. His research advances how machines can learn, predict or control, and do so at scale in an efficient, principled, and interpretable manner. His work in machine learning extends from foundational theory to modern applications, focusing especially on statistical inference and estimation tasks that lie at the heart of complex learning problems. He designs new methods, theory and algorithms to automate the use and generation of semi-structured data such as natural language text, images, molecules, or strategies. Jaakkola applies and develops algorithms to solve multi-faceted recommender, retrieval, or inferential tasks (particularly in biomedical contexts), design and optimize molecules or reactions for drug design, and model strategic, game theoretic interactions. His recent work heavily focuses on diffusion models, protein structure prediction, molecular design, and generative AI, with significant publications in top conferences including ICML, NeurIPS, and ICLR. His scientific contributions span multiple disciplines with significant impact in both theoretical machine learning and practical applications in computational biology and chemistry, including notable work on antibiotic discovery published in Cell. Current advisees: Julia Balla, Bowen Jing, Hannes Stärk, Peter Holderrieth, Chenyu Wang Recent graduates: Gabriele Corso (Boltz PBC), Ezra Erives (DE Shaw), Jason Yim (Xaira) Jaakkola maintains an active research program through MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Institute for Data, Systems, and Society (IDSS), with his office located in the Stata Center (32-G470). His work bridges theoretical machine learning with practical applications, making significant contributions to both the academic field and potential real-world impact in healthcare and drug discovery.
Farzad Mashayek is a Professor and Department Head of Aerospace and Mechanical Engineering at the University of Arizona, College of Engineering. He is a member of the Graduate Faculty and leads the Computational Multiphase Transport Laboratory. His research integrates high-fidelity simulations, machine learning, and experimental validation across diverse domains in fluid dynamics and energy systems. Educational Background: PhD in Mechanical Engineering, State University of New York at Buffalo, Buffalo, NY MS in Mechanical Engineering, Sharif University of Technology, Tehran, Iran BS in Mechanical Engineering, Sharif University of Technology, Tehran, Iran His research interests include turbulent reacting flows, plasma dynamics, electrostatic atomization, solid-ion and lithium batteries, computational fluid dynamics, and machine learning applications in engineering. He employs high-order spectral element methods, phase-field modeling, and deep neural networks to study complex multiphysics phenomena such as drop impact, battery degradation, and turbulence modeling. The recent publications reflect a strong trend toward integrating machine learning with multiphysics simulations, particularly in battery safety (thermal runaway prediction), materials characterization (STEM image analysis), and fluid dynamics (modal analysis of turbulence). His work often involves collaboration with experimental groups to validate models, especially in dental aerosol suppression and electrohydrodynamics. Scientific Awards: Sustained Service Award, American Institute of Aeronautics and Astronautics (AIAA), Spring 2022 Best Presentation Award, The 20th International Conference on Computational Mathematics, Parallel and Distributed Computing, Summer I 2018 Dr. Mashayek has secured funding from NSF (GOALI program) for controlled coating via charged droplet deposition. He advises graduate students and postdoctoral researchers in computational mechanics and energy systems, fostering interdisciplinary research. He has contributed to engineering education, particularly during the pandemic, with active learning strategies in online instruction. He leads a dynamic research team focused on advancing simulation tools and applying them to real-world challenges in energy, manufacturing, and public health.
California Institute of Technology (Caltech)United States
Richard M. Murray is the Thomas E. and Doris Everhart Professor of Control and Dynamical Systems and Bioengineering at the California Institute of Technology (Caltech). He holds a B.S. from Caltech (1985), M.S. from UC Berkeley (1988), and Ph.D. from UC Berkeley (1990). He has served in academic roles from Assistant Professor (1991–1997) to his current endowed professorship. He chaired the Engineering and Applied Science division (2000–2005) and Biology and Biological Engineering (2020–2024). His research focuses on feedback control in biological and autonomous systems, synthetic cells, and networked control systems. Collaborators include experts in robotics, synthetic biology, and systems biology. Key awards include the IEEE Control Systems Award and election to the National Academy of Engineering. His educational contributions span courses on control systems, robotics, and bioengineering. Current research projects include the Developer Cell initiative (Sloan Foundation), layered testing for autonomous systems (AFOSR), and microbiome-based environmental solutions (CHARMME, ARO). He advises numerous graduate students and postdocs, with notable alumni in academia and industry. Labs include facilities in Keck and Steele laboratories at Caltech. His work bridges control theory, synthetic biology, and autonomous systems to address societal challenges like environmental monitoring and safe autonomy.
Gianluca Iaccarino is a Professor of Mechanical Engineering at Stanford University and the Robert Bosch Chairholder. He serves as Director of the PSAAP Center and leads large-scale computational research initiatives in uncertainty quantification, exascale computing, and multiphysics simulations. His academic journey includes a PhD in Mechanical Engineering from Politecnico di Bari (2005), postdoctoral work at Stanford's Center for Turbulence Research, and progression from Research Engineer to full Professor. Education : PhD (Politecnico di Bari), MS/BS in Aeronautical Engineering (University of Naples) Research : Computational engineering, turbulence modeling, uncertainty quantification, biomedical fluid dynamics, and exascale-ready algorithms Publications : 15+ recent articles focus on turbulence modeling, data-driven simulations, and uncertainty quantification across diverse applications in aerospace, biomedical, and energy systems Awards : PECASE (2010), APS Fellow (2019), multiple best paper awards (AIAA, ASME), Terman Fellow (2007) Students : Advises doctoral and master's students in mechanical engineering and computational methods Leadership : Director of PSAAP Center (2014-present), Chair of Mechanical Engineering Department (2024-present)
