Image Processing, Neuroimaging, Machine Learning and Assistive Technology

See more about Dr.Adjouadi here: Malek Adjouadi – People – ECE – Florida International University – FIU

IoT Security

Dr. Kemal Akkaya is a full professor in the Department of Electrical and Computer Engineering with a joint courtesy appointment in the School of Computer and Information Sciences at Florida International University. Dr. Akkaya leads the Advanced Wireless and Security Lab (ADWISE) in the ECE Dept. His current research interests include security and privacy, internet-of-things, and cyber-physical systems. In this research he employs methods from machine learning to several specific problems. Mainly, machine learning and deep learning methods are used for digital forensics applications including multimedia and cryptocurrency forensics. Similar techniques are used for device fingerprinting such as drones. Dr. Akkaya also utilizes machine learning and data analytics for blockchain applications to analyze any transactions. Finally, Dr. Akkaya employs secure multi-party computation techniques for enabling privacy-preserving machine learning applications.

See more about Dr. Akkaya here: Dr. Kemal Akkaya (fiu.edu)

Ariel Baron-Robbins, an interdisciplinary artist and academic specializing in Digital Media and Drawing, teaches Animation at Florida International University as a Visiting Associate Teaching Professor. Holding an MFA in Interdisciplinary Studies from the University of South Florida, she has over a decade of experience teaching Drawing and Digital Art in South Florida and has exhibited her artwork widely.
She is integrating Generative Artificial Intelligence into her Intermediate Animation, Advanced Animation, and Story Development courses. She requires students to use GAN programs in the classroom for creating animations and to assist with conceptualization and research. She recorded live on X and for a podcast during her Intermediate/Advanced class in 2024 with artist and podcaster Whizpill, titled “Animation & AI: Skills & Tools of the Future Taught in Universities.”She has recently participated in a panel at FIU’s Ratcliffe Incubator Space, titled “Before and After,” where she discussed and demonstrated the impact of using Generative AI. Additionally, she was featured in a discussion with Stephanie Tripp from the University of Tampa, titled “Discussion with Curator, AI, NFTs, Art and the Metaverse,” held at Tempus Projects in Tampa, FL.

Digital Signal Processing, Artificial Neural Networks, Image Processing, and Micro Processors 

Armano Barreto Graduated with a Bachelors Degree in Electrical-Mechanical Engineering from the National Autonomous University of Mexico (UNAM ), in 1987. He obtained the M.S.E.E. Degree from Florida International University in August 1989 and the Ph.D. In Electrical Engineering from University of Florida , Gainesville, in May 1993. Then he spent one more year in Gainesville as a Post-Doctoral Associate with the Computational Neuro-Engineering Laboratory ( CNEL) / UF Brain Institute. Through his Ph. D. and Post-Doc studies he applied Spatio-Temporal DSP techniques to the real-time detection of specific (abnormal) features in multi-channel Electroencephalogram (EEG) Data

See more about Dr. Barreto here: DSP (fiu.edu)

Security, Privacy, and Distributed Systems

Bogdan Carbunar is an Associate Professor in the Knight Foundation School of Computing and Information Sciences at FIU, and directs the Cyber Security and Privacy Research (CaSPR) Lab, where he develops secure and usable systems.  His research interests are at the intersection of security, privacy and distributed systems, where he derives novel insights through the use of machine learning, applied cryptography and user studies. His recent interests include fraud and abuse detection in online systems (e.g., Google, Facebook, Yelp), mobile authentication and cryptocurrency-based censorship resistance. He has published papers in top tier conferences and journals (see link above for details) some of which have received best paper awards. He holds a PhD in computer science from Purdue University and a BS from Politehnica University Bucharest.

See more about Dr.Carbunar here: Bogdan Carbunar (fiu.edu)

Dr. Deanne Butchey is a Teaching Professor in the Department of Finance, former Chair of FIU Faculty Senate, Member of the FIU Board of Trustees, and Associate Dean of Accreditation at FIU Business.

Deanne holds a PhD in Finance from FIU, an MBA and an Honors Bachelor of Arts in Econometrics from Canada’s York University’s Schulich School of Business, and a general BA in Economics from University of Western Ontario. Before joining FIU, Deanne worked as an Investment Banker and Financial Services Stock Research Analyst at Credit Suisse First Boston in their Toronto offices.

Based on her past experiences and current research, she has integrated the use of AI in the teaching of Finance. In her class students work with AI-powered algorithms and software to analyze vast amounts of financial data, identify patterns, and answer economic/finance related questions. Some of the AI applications include helping investment and commercial banks detect and mitigate risks and identify fraud, providing timely insights to decision-makers and regulators. Students incorporate AI algorithms to analyze market trends, historical data, investor preferences, social media posts, and other sources of information to gauge market sentiment and make informed investment decisions.

Current Projects

• Project Title: Teaching Big Data and Analytics in Finance in the MSF program
  •  Annually, two cohorts of students work with IBM Watson, SAS Analytics, Python and other tools to answer a burning question related to Finance or Financial Economics. They incorporate the theory of Finance with AI powered algorithms to prepare themselves for the Financial Services world that is increasingly incorporating AI tools to predict the future and prescribe paths for optimal decision-making.

Hyeyoung Hah received a B.S. degree in Statistics from Dongguk University, Seoul, South Korea in 2000, an M.S. degree in Consumer Behavior from Purdue University, West Lafayette, IN, USA in 2008, and a Ph.D. degree in Information Systems from Emory University, Atlanta, GA, USA in 2016.  She is currently an Associate Professor at Florida International University, Miami, FL, USA.  She has authored 18  publications in the referred journals and conference proceedings. She is a recipient of the best paper runner-up award at the International Conferences on Information Systems (ICIS) in 2012. Recently, her work was nominated for the best paper award at the Hawaiian International Conference on System Sciences (HICSS) in 2020. Her research interests lie in the adoption and impact of digital innovation on various performance metrics in the contexts of healthcare, mobile platforms, finance, and e-commerce in general, and AI-driven decision-making and Internet of Medical Things (IoMT) in specific.

Projects:

• Empowering Citizens’ Self-Health Management via Shoe-based Sensor Technology and Internet of Medical Things
  • Brief Description:  The project focuses on enhancing citizens’ self-health management skills in everyday situations. The primary objectives include designing and evaluating citizen-focused Internet of Medical Things (IoMT) artifacts and identifying barriers and enablers of citizens’ IoMT use. The methodologies employed in the project encompass Applied Science Engineering (AS/E), Design Science, Empirical Econometrics, and Psychometrics. The ultimate impact of the project is to improve the accessibility and applicability of IoMT technologies and devices, enabling everyday citizens to manage their health effectively between doctor’s visits.

Jong Lee is an assistant professor in the Department of Information Systems and Business Analytics (ISBA) at the College of Business. His current research interests involve examining the socioeconomic impact of emerging technology, including the impact of AI applications in the domains of digital platforms and healthcare. He applies quantitative techniques rooted in economics to address business-related challenges. He received his Ph.D. in Business Administration from Simon Business School at the University of Rochester in 2023, with a focus on Computer Information Systems. He also holds a master’s degree in Industrial Engineering.

Current Projects:

• Quasi-experimental analysis on behavioral differences between humans and social bots
  • Brief Description: Conducting a quasi-experiment to understand how social bots and humans alter their behavior in response to changes in platform expression constraints. This study has meaningful implications for the identification and management of social bots on social media platforms. 

Modeling and Simulation, Parallel Discrete-Event Simulation, Performance Modeling and Simulation of Computer Systems and Computer Networks

Jason Liu is an Eminent Scholar Chaired Professor at the Knight Foundation School of Computing and Information Sciences, Florida International University (FIU) in Miami, Florida, USA.  Previously, he was a Research Scientist at Dartmouth ISTS in 2003, a Postdoctoral Researcher at University of Illinois, Urbana-Champaign during 2003-2004. He held a honorary position as Visiting Professor at Tsinghua University, China, and was a Visiting Scientist at Los Alamos National Laboratory (LANL) and Université Pierre et Marie Curie (UPMC)/Laboratory of Information, Networking and Communication Sciences (LINCS). Jason Liu received a B.S. degree from Beijing University of Technology in China in 1993, an M.S. degree from College of William and Mary in 2000, and a Ph.D. degree from Dartmouth College in 2003.

Jason Liu’s research focuses on modeling and simulation, parallel discrete-event simulation, performance modeling and simulation of computer systems and computer networks. He currently serves on the Editorial Board of ACM Transactions on Modeling and Computer Simulation (TOMACS), SIMULATION, Transactions of the Society for Modeling and Simulation International, and IEEE Networking Letters. He is also on the Steering Committee of ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (SIGSIM-PADS). He served as General Chair or Program Chair for several conferences in related areas. Jason Liu is an NSF CAREER awardee in 2006 and an ACM Distinguished Scientist in 2014. His research has been supported by various funding agencies, including NSF, DOE, DOD, DHS, and NIH.

See more about Dr. Liu here: Home Page – Jason Liu (fiu.edu)

Dr. Ozde Oztekin commenced her academic career at the University of Kansas and currently holds a position as a full professor and an Eminent Scholar at the Florida International University. Dr. Oztekin’s research primarily centers around corporate finance, international finance, and banking. A specific area of interest for her has been understanding the decision-making processes of corporate firms and financial institutions. Recently, Dr. Oztekin has delved into the application of AI in finance. This is highlighted by her work on the role of machine learning models in comprehending capital structure dynamics and her research analyzing the influence of COVID-19 policy responses on local economic and health outcomes. She has been a prolific contributor to various reputable journals and has displayed a strong commitment to quality education, evident from her diverse teaching portfolio that spans across multiple levels and modalities. Beyond her teaching and research roles, Dr. Oztekin is an active participant in the academic community, refereeing for several journals, undertaking editorial responsibilities, and engaging in conference activities.

Publications and Book Chapters:

1. Can machines learn capital structure dynamics?

This project investigates the potential of machine learning models in predicting leverage more accurately than linear models. The study showcases that machine learning models, especially random forests, significantly boost prediction accuracy. These models were found to identify a broader set of leverage determinants, highlighting factors like market-to-book, industry median leverage, cash and equivalents, and more. The research underscored machine learning’s capability to provide a more precise target estimation and emphasize the primary drivers of leverage adjustments.

Links: https://www.sciencedirect.com/science/article/abs/pii/S0929119921001954

2. The impact of COVID-19 and its Policy Responses on Local Economy and Health Conditions

    In collaboration with Ali Gungoraydinoglu and Ilke Oztekin, this research utilizes machine learning techniques to delve into the consequences of U.S. states’ lockdown measures meant to curb the spread of COVID-19. The study aims to unveil the marginal health benefits juxtaposed with the economic ramifications associated with social distancing. Through our analytical models, it was observed that while lockdowns alleviated disease severity, they also precipitated significant economic downturns. The decline in health conditions further impacted the labor supply, financial stability, and overall economic productivity. Interestingly, while the economic fallout from lockdowns surpassed the economic repercussions directly from the disease, health conditions presented a more reliable predictor for economic contractions.

Links: https://www.mdpi.com/1911-8074/14/6/233

3. “Coronavirus Pandemic and its Economic and Human Capital Costs in US States”

This book chapter expands upon a previous study, exploring the balance between health outcomes and economic impacts in US states during the COVID-19 pandemic. Machine learning assessment indicated that disease severity is a stronger predictor of real local economic contraction than mandatory social distancing policy measures. The research underlines how state policy measures and health conditions influenced unemployment, business bankruptcy, and overall economic activity.

Links: https://stm.bookpi.org/NIEBM-V3/article/view/5069

Dr. Esmaeil Zadeh has established a line of research at the intersection of information systems and health informatics, focusing on the impact of health information technology (HIT) and emerging technologies on the performance of health systems, improving the quality and efficiency of care delivery and enhancing the engagement of patients in managing their care. His main research objective is to study various HIT systems to offer technological solutions for enhancing health information exchange (HIE) across providers, care coordination, healthcare providers’ workflows, system security, patient safety, the completeness and accuracy of medical records, and healthcare interoperability standards. He is also interested in other research domains, including Nonfungible Tokens (NFTs), blockchain-based platforms, Artificial Intelligence (AI) and Machine Learning (ML), Robotics, Health app development, and Metaverse. His work appears in premier Information Systems and Health Informatics journals such as Information and Management, International Journal of Medical Informatics, Journal of Medical Internet Research, Journal of Biomedical Informatics, BMC Medical Informatics and Decision Making, Communications of the Association for Information Systems, Computers and Security, Information Technology & People, and Methods of Information in Medicine. He was appointed to a Knight-Ridder Center Research Fellowship in August 2021. He also received the Fulbright Award from the Canada-US Fulbright Program for the 2022-2023 academic year.

Distributed Sensor Networks, Grid Computing, Computer Networks, Image Processing for Spatial Data, Multimedia Databases

Dr. Prabakar developed a scheme to access vast amount of spatial data from a semantic database and flyover the data in real-time – this emerged as TerraFly software from High Performance Database Research Center, FIU. He has also designed dynamic mosaicking algorithms for spatial images and integrated vector GIS data with spatial data sets. Towards external funding, eight grant proposals were funded for a total amount of $13.7M with Dr. Prabakar’s role in these proposals as Principal Investigator, Co-Investigator, or Senior Investigator. Currently, Dr. Prabakar is working with a team of his colleagues on a fault-tolerant distributed computing grid with large number of sensors.

See more about Dr. Prabakar here: Prabu’s Home Page (fiu.edu)

Smart Grids, Large-Scale Data Analysis, Advance Metering Infrastructure, Smart City Infrastructure and Cybersecurity

Dr. Arif Sarwat is a professor in the department of Electrical and Computer Engineering and also the Director of FIU-FPL Solar Research Center. Dr. Sarwat received his master’s degree from University of Florida, Gainesville, his Ph.D. from University of South Florida, Tampa and currently leads The Energy, Power and Sustainability Group at FIU. Dr. Sarwat’s research interests include smart grids, Plug-in Hybrid and Electric Vehicle (PHEV & EV Systems), high penetration renewable systems, grid resiliency, large-scale data analysis, advance metering infrastructure, smart city infrastructure and cybersecurity. His outstanding contribution led him to become the recipient of NSF Career Award in 2016 and multiple federal and industry research awards. He was the author/co-author of conference best paper awards at the resilience week in 2017 and a journal best paper award in 2016 from Modern Power Systems and Clean Energy journal. Dr. Sarwat worked at Siemens for more than nine years, winning three recognition awards. He is chair of IEEE Miami Section VT and Communication since 2012.

See more about Dr. Sarwat here: Arif Sarwat – People – ECE – Florida International University

Dr. Usman studies consumers’ responses to new technologies in marketing using experimental research designs. He is currently working on projects related to generative AIs and has published an article on Behavioral Research on AI in Marketing recently in Foundations and Trends in Marketing.

Projects:

• Artificial Intelligence in Marketing and Consumer Behavior Research
  • Brief Description: This project reviewed the behavioral research on AI in marketing and presented a synthesis of what the current trends are, including the predominant dependent variables being measured by researchers, theories being used to explain the findings and the types of behavioral research design used in the research. The manuscript concludes with our key observations and identifies future research directions, raises interesting questions that are yet to be answered by behavioral research on AI in marketing, and suggests means to enhance the methodology and behavioral research designs currently being used in marketing.
    View Article: TaeWoo Kim, Umair Usman, Aaron Garvey and Adam Duhachek (2023), “Artificial Intelligence in Marketing and Consumer Behavior Research”, Foundations and Trends® in Marketing: Vol. 18: No. 1, pp 1-93.

• The Persuasive Power of AI Ingratiation: A Persuasion Knowledge Theory Perspective
  • Brief Description:  This research examines the emerging marketing tactic of AI ingratiation of humans and reveals that AI ingratiation leads to increased consumer acceptance of product recommendations. Using behavioral experiments, including online studies and a real human-AI interaction, we show that these ingratiation effects are moderated by the extent to which AI is anthropomorphized. Our findings advance the current knowledge about human-AI interaction in consumption contexts and finds support for our hypotheses based on the Persuasion Knowledge Model and Anthropomorphism literature. Specifically, we find that consumers perceive ingratiation by human-like (vs. machine-like) AI systems to be more driven by ulterior motives, thereby activating consumer defense mechanisms against ingratiation attempts. Our theory and findings elucidate how AI design features serve to strengthen or weaken consumer resistance to persuasion.

Dr. Lassar is a Professor of Marketing and holds the Ryder Systems Professor chair at Florida International University. After a 13-year business career holding various marketing and management positions in the U.S. and in Europe Dr. Lassar started his academic career in marketing. Dr. Lassar earned his Ph.D. and MBA degrees from the University of Southern California. A native of Germany, he also holds a degree in Industrial Engineering (Dipl.-Ing.) with a double major in mechanical engineering and business administration from the Technische Universität Berlin, Germany.  

Dr. Lassar taught and researched on four continents with stops at the London Business School, the Owen Graduate School of Management at Vanderbilt University, the Whittemore School of Business and Economics at the University of New Hampshire, and the University of Miami. In August 1998 he joined Florida International University where he served as the Associate Dean for the Alvah H. Chapman Jr. Graduate School of business, the Chair of the Marketing Department, the Director of the Ryder Center for Supply Chain Management and the Academic Director for the Executive MBA program. 

His research focuses on services marketing and marketing strategy in value chains as well as value issues complex business-to-business relationships and sustainability.  His research projects integrate domestic and international business dimensions with consideration of disruptive innovation aspects. His work has been published in the Journal of Marketing, Journal of Business Logistics, Journal of Business Research, Journal of Retailing, Journal of Business Ethics, Journal of Business Venturing and the Strategic Management Journal among others.  

Projects:

• Humanoid robots in home healthcare and their impact on perceived wellbeing
  • Brief Description: The presence of robots in the services industry is transforming the way we experience customer service. Service robots can be utilized to improve customer experience, service quality, and productivity all at once because to their integration with artificial intelligence (AI) and increasingly cost-efficient and scalable nature.  Our research aims to shed light on the advantages and disadvantages of utilizing service robots in healthcare settings by examining the effect of service robots on patients’ well-being. As a result, healthcare practitioners may be better equipped to decide whether to use service robots in their facilities. In addition, this would help comprehend how social cognition and resistance to service robots relates to the well-being of customers when it comes to service robots.
    Read more: Meet Grace, the ultra-lifelike nurse robot