My research focuses on the intended and unintended consequences of language in financial markets. I examine this relationship across a number of contexts, such as the Federal Reserve, initial public offerings, and mergers and acquisitions. More broadly, my work aims to develop new theoretical and methodological approaches to understand the role of language in society.
Professor Saigal has held faculty positions at the Haas School of Business, Berkeley and the department of Industrial Engineering and Management Sciences at Northwestern University, has been a researcher at the Bell Telephone Laboratories and numerous short term visiting positions. He currently teaches courses in Financial Engineering. In the recent past he taught courses in optimization, and Management Science. His current research involves data based studies of operational problems in the areas of Finance, Transportation, Renewable Energy and Healthcare, with an emphasis on the management and pricing of risks. This involves the use of data analytics, optimization, stochastic processes and financial engineering tools. His earlier research involved theoretical investigation into interior point methods, large scale optimization and software development for mathematical programming. He is an author of two books on optimization and large set of publications in top refereed journals. He has been an associate editor of Management Science and is a member of SIAM, AMS and AAAS. He has served as the Director of the interdisciplinary Financial Engineering Program and as the Director of Interdisciplinary Professional Programs (now Integrative Design + Systems) at the College of Engineering.
My research focus is on the development and application of machine learning tools to large scale financial and unstructured (textual) data to extract, quantify and predict risk profiles and investment grade rating of private and public companies. Example datasets include social media and financial aggregators such as Bloomberg, Pitchbook, and Privco.
Henry Liu, PhD, is Professor of Civil and Environmental Engineering in the College of Engineering and holds a secondary appointment of Research Professor in the U-M Transportation Research Institute (UMTRI). Prof. Liu is also Director of the Center for Connected and Automated Transportation (Regional 5 UTC).
Prof. Liu’s research is in the areas of traffic network monitoring, modeling and control. His recent work has focused on traffic flow modeling and simulation, traffic signal control and optimization, traffic management under network disruptions and equilibrium traffic assignment. Prof. Liu is the co-founder and chairman of the advisory board for SMART Signal Technologies. The SMART Signal (Systematic Monitoring of Arterial Road Traffic Signals) is a real-time arterial performance monitoring system that uses traffic data from existing signal systems. SMART Signal simultaneously collects event-based high-resolution traffic data from multiple intersections and generates real-time signal performance measures, including arterial travel time, number of stops, queue length, intersection delay and level of service. “Traffic engineers can use this information to improve traffic flow on roads controlled by traffic lights—reducing congestion and saving drivers both time and fuel. SMART Signal could also give drivers a more accurate prediction of travel times by accounting for time spent waiting at traffic lights,” states the University of Minnesota Intelligent Transportation Systems website. The system is now deployed at more than 100 intersections on major arterial corridors in Minnesota and Pasadena, California. Arterial corridors are roads where many cars can move from urban centers to high-capacity freeways.
Jason Mars is a professor of computer science at the University of Michigan where he directs Clarity Lab, one of the best places in the world to be trained in A.I. and system design. Jason is also co-founder and CEO of Clinc, the cutting-edge A.I. startup that developed the world’s most advanced conversational AI.
Jason has devoted his career to solving difficult real-world problems, building some of the worlds most sophisticated salable systems for A.I., computer vision, and natural language processing. Prior to University of Michigan, Jason was a professor at UCSD. He also worked at Google and Intel.
Jason’s work constructing large-scale A.I. and deep learning-based systems and technology has been recognized globally and continues to have a significant impact on industry and academia. Jason holds a PhD in Computer Science from UVA.
My research interests are in developing inter-disciplinary knowledge in System Informatics, as the basis for study of complex system problems with the fusion of theory, computation, and application components adopted from Systems and Informatics fields. In this framework, a complex system such as the supply chain is posited as a System-of-Systems; i.e., a collection of individual business entities organized as a composite system with their resources and capabilities pooled to obtain an interoperable and synergistic system, possessing common and shared goals and objectives. Informatics facilitates coordination and integration in the system by processing and sharing information among supply chain entities for improved decision-making.
A common theme of my research is the basic foundation of universality of system and the realization that what makes it unique is its environment. This has enabled to categorize problems, designs, models, methodologies, and solution techniques at macro and micro levels and develop innovative solutions by coordinating these levels in an integrated environment.
My goal is to study the efficacy of the body of knowledge available in Systems Theory, Information Science, Artificial Intelligence & Knowledge Management, Management Science, Industrial Engineering and Operations Research fields; applied uniquely to issues and problems of complex systems in the manufacturing and service sectors.
Theoretical work investigated by me in this research thrust relates to:
- Developing Generalized System Taxonomies and Ontologies for complex systems management.
- Experimenting with Problem Taxonomies for design and modeling efficiencies in complex system networks.
- Developing methodologies, frameworks and reference models for complex systems management.
- Computation and application development focused on developing algorithms and software development for:
- Supply chain information system and knowledge library using Web-based technology as a dissemination tool.
- Integration with Enterprise Resource Planning modules in SAP software.
- Supply chain management problem-solving through application of problem specific simulation and optimization.
My research has extended to application domains in healthcare, textiles, automotive, and defense sectors. Problems and issues addressed relate to health care management, operationalizing of sustainability, energy conservation, global logistics management, mega-disaster recovery, humanitarian needs management, and entrepreneurship management.
Currently, my application focus is on expanding the breadth and depth of inquiry in the healthcare domain. Among the topics being investigated are: (1) the organization and structure of health care enterprises; and (2) operations and strategies that relate to management of critical success factors, such as costs, quality, innovation and technology adoption by health care providers. Two significant topics that I have chosen to study with regard to care for elderly patients suffering from chronic congestive heart failure and hypertension are: (1) the design of patient-centered health care delivery to improve quality of care; and (2) managing enhanced care costs due to readmission of these patients.
Data science applications: Real-time data processing in supply chains, Knowledge portals for decision-making in supply chains, information sharing for optimizing patient-centered healthcare delivery
My lab creates systems that use a combination of both human and machine computation to solve problems quickly and reliably. We have introduced the idea of continuous real-time crowdsourcing, as well as the ‘crowd agent’ model, which uses computer-mediated groups of people submitting input simultaneously to create a collective intelligence capable of completing tasks better than any constituent member.